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

Resting-state EEG and MEG biomarkers of pathological fatigue - A transdiagnostic systematic review

Fatigue is a highly prevalent and disabling symptom of many disorders and syndromes, resulting from different pathomechanisms. However, whether and how different mechanisms converge and result in similar symptomatology is only partially understood, and transdiagnostic biomarkers that could further the diagnosis and treatment of fatigue are lacking. We, therefore, performed a transdiagnostic systematic review (PROSPERO: CRD42022330113) of quantitative resting-state electroencephalography (EEG) and magnetoencephalography (MEG) studies in adult patients suffering from pathological fatigue in different disorders. Studies investigating fatigue in healthy participants were excluded. The risk of bias was assessed using a modified Newcastle-Ottawa Scale. Semi-quantitative data synthesis was conducted using modified albatross plots. After searching MEDLINE, Web of Science Core Collection, and EMBASE, 26 studies were included. Cross-sectional studies revealed increased brain activity at theta frequencies and decreased activity at alpha frequencies as potential diagnostic biomarkers. However, the risk of bias was high in many studies and domains. Together, this transdiagnostic systematic review synthesizes evidence on how resting-state M/EEG might serve as a diagnostic biomarker of pathological fatigue. Beyond, this review might help to guide future M/EEG studies on the development of fatigue biomarkers.

ME/CFS and Long COVID share similar symptoms and biological abnormalities: road map to the literature

Some patients remain unwell for months after "recovering" from acute COVID-19. They develop persistent fatigue, cognitive problems, headaches, disrupted sleep, myalgias and arthralgias, post-exertional malaise, orthostatic intolerance and other symptoms that greatly interfere with their ability to function and that can leave some people housebound and disabled. The illness (Long COVID) is similar to myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) as well as to persisting illnesses that can follow a wide variety of other infectious agents and following major traumatic injury. Together, these illnesses are projected to cost the U.S. trillions of dollars. In this review, we first compare the symptoms of ME/CFS and Long COVID, noting the considerable similarities and the few differences. We then compare in extensive detail the underlying pathophysiology of these two conditions, focusing on abnormalities of the central and autonomic nervous system, lungs, heart, vasculature, immune system, gut microbiome, energy metabolism and redox balance. This comparison highlights how strong the evidence is for each abnormality, in each illness, and helps to set priorities for future investigation. The review provides a current road map to the extensive literature on the underlying biology of both illnesses.

Differences of EEG Frequency and Spatial Parameters in Depressive Female Adolescents With Suicidal Attempts and Non-suicidal Self-injuries

Background. Both non-suicidal self-injuries (NSSIs) and suicidal attempts (SAs) in adolescence represent significant risk factors for consequent suicide, but neurophysiological markers and predictors of these two forms of auto-aggressive behavior have been studied insufficiently. Objective. The aim of the study was to identify the differences of electroencephalographic (EEG) frequency and spatial parameters between depressive female adolescents with solely NSSI, and with combined NSSI + SA behavior in their history. Methods. The study included 45 female depressive in-patients aged 16-25 years. Baseline resting EEG spectral power, asymmetry, and coherence were analyzed in 8 narrow frequency sub-bands. Results. In the NSSI + SA subgroup (n = 24), the spectral power of parietal-occipital alpha-2 (9-11 Hz) was higher than in the NSSI subgroup, its focus was localized in the right hemisphere, and alpha-3 (11-13 Hz) spectral power was higher than alpha-1 (8-9 Hz). In the NSSI subgroup (n = 21) alpha-1 spectral power was higher than alpha-3, and foci of alpha-2 and alpha-3 were localized in the left hemisphere. EEG coherence was also higher in the NSSI + SA subgroup than in the NSSI subgroup, especially in frontal-central-parietal regions. Conclusions. The spatial distribution of the EEG frequency components in the NSSI + SA subgroup reflects the greater activation of the left hemisphere that is more typical for the EEG of individuals with an increased risk for suicide. In the NSSI subgroup, the right hemisphere is relatively more activated, and EEG coherence is lower, which is more typical for EEG in depressive disorders. The results obtained suggested the use of EEG to clarify the degree of suicidal risk in depressive female adolescents with NSSI.

Autism, spectrum or clusters? An EEG coherence study

BACKGROUND

Autism prevalence continues to grow, yet a universally agreed upon etiology is lacking despite manifold evidence of abnormalities especially in terms of genetics and epigenetics. The authors postulate that the broad definition of an omnibus 'spectrum disorder' may inhibit delineation of meaningful clinical correlations. This paper presents evidence that an objectively defined, EEG based brain measure may be helpful in illuminating the autism spectrum versus subgroups (clusters) question.

METHODS

Forty objectively defined EEG coherence factors created in prior studies demonstrated reliable separation of neuro-typical controls from subjects with autism, and reliable separation of subjects with Asperger's syndrome from all other subjects within the autism spectrum and from neurotypical controls. In the current study, these forty previously defined EEG coherence factors were used prospectively within a large (N = 430) population of subjects with autism in order to determine quantitatively the potential existence of separate clusters within this population.

RESULTS

By use of a recently published software package, NbClust, the current investigation determined that the 40 EEG coherence factors reliably identified two distinct clusters within the larger population of subjects with autism. These two clusters demonstrated highly significant differences. Of interest, many more subjects with Asperger's syndrome fell into one rather than the other cluster.

CONCLUSIONS

EEG coherence factors provide evidence of two highly significant separate clusters within the subject population with autism. The establishment of a unitary "Autism Spectrum Disorder" does a disservice to patients and clinicians, hinders much needed scientific exploration, and likely leads to less than optimal educational and/or interventional efforts.

A neuro-inflammatory model can explain the onset, symptoms and flare-ups of myalgic encephalomyelitis/chronic fatigue syndrome

Abstract A neuro-inflammatory model is proposed to explain the onset, symptoms and perpetuation of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) via characteristic flare-ups (relapses). In this article, I explore the proposition that a range of triggers (intense physiological stressors such as severe viral infections, chemical toxin exposure or emotional trauma) in ME/CFS-predisposed people causes disruption in the neural circuitry of the hypothalamus (paraventricular nucleus), which induces a neuro-inflammatory reaction in the brain and central nervous system of ME/CFS patients, via over-active innate immune (glial) cells. Resulting dysfunction of the limbic system, the hypothalamus and consequently of the autonomic nervous system can then account for the diverse range of ME/CFS symptoms. Ongoing stressors feed into a compromised (inflamed) hypothalamus and if a certain (but variable) threshold is exceeded, a flare-up will ensue, inducing further ongoing neuro-inflammation in the central nervous system, thus perpetuating the disease indefinitely.

Poor self-reported sleep quality and health-related quality of life in patients with chronic fatigue syndrome/myalgic encephalomyelitis

Non-restorative sleep is a hallmark symptom of chronic fatigue syndrome/myalgic encephalomyelitis. However, little is known about self-reported sleep disturbances in these subjects. This study aimed to assess the self-reported sleep quality and its impact on quality of life in a Spanish community-based chronic fatigue syndrome/myalgic encephalomyelitis cohort. A prospective cross-sectional cohort study was conducted in 1,455 Spanish chronic fatigue syndrome/myalgic encephalomyelitis patients. Sleep quality, fatigue, pain, functional capacity impairment, psychopathological status, anxiety/depression and health-related quality of life were assessed using validated subjective measures. The frequencies of muscular, cognitive, neurological, autonomic and immunological symptom clusters were above 80%. High scores were recorded for pain, fatigue, psychopathological status, anxiety/depression, and low scores for functional capacity and quality of life, all of which correlated significantly (all p < 0.01) with quality of sleep as measured by the Pittsburgh Sleep Quality Index. Multivariate regression analysis showed that after adjusting for age and gender, the pain intensity (odds ratio, 1.11; p <0.05), psychopathological status (odds ratio, 1.85; p < 0.001), fibromyalgia (odds ratio, 1.39; p < 0.05), severe autonomic dysfunction (odds ratio, 1.72; p < 0.05), poor functional capacity (odds ratio, 0.98; p < 0.05) and quality of life (odds ratio, 0.96; both p < 0.001) were significantly associated with poor sleep quality. These findings suggest that this large chronic fatigue syndrome/myalgic encephalomyelitis sample presents poor sleep quality, as assessed by the Pittsburgh Sleep Quality Index, and that this poor sleep quality is associated with many aspects of quality of life.

A unique pattern of cortical connectivity characterizes patients with attention deficit disorders: a large electroencephalographic coherence study

BACKGROUND

Attentional disorders (ADD) feature decreased attention span, impulsivity, and over-activity interfering with successful lives. Childhood onset ADD frequently persists to adulthood. Etiology may be hereditary or disease associated. Prevalence is 5% but recognition may be 'overshadowed' by comorbidities (brain injury, mood disorder) thereby escaping formal recognition. Blinded diagnosis by MRI has failed. ADD may not itself manifest a single anatomical pattern of brain abnormality but may reflect multiple, unique responses to numerous and diverse etiologies. Alternatively, a stable ADD-specific brain pattern may be better detected by brain physiology. EEG coherence, measuring cortical connectivity, is used to explore this possibility.

METHODS

Participants: Ages 2 to 22 years; 347 ADD and 619 neurotypical controls (CON). Following artifact reduction, principal components analysis (PCA) identifies coherence factors with unique loading patterns. Discriminant function analysis (DFA) determines discrimination success differentiating ADD from CON. Split-half and jackknife analyses estimate prospective diagnostic success. Coherence factor loading constitutes an ADD-specific pattern or 'connectome'.  RESULTS: PCA identified 40 factors explaining 50% of total variance. DFA on CON versus ADD groups utilizing all factors was highly significant (p≤0.0001). ADD subjects were separated into medication and comorbidity subgroups. DFA (stepping allowed) based on CON versus ADD without comorbidities or medication treatment successfully classified the correspondingly held out ADD subjects in every instance. Ten randomly generated split-half replications of the entire population demonstrated high-average classification success for each of the left out test-sets (overall: CON, 83.65%; ADD, 90.07%). Higher success was obtained with more restricted age sub-samples using jackknifing: 2-8 year olds (CON, 90.0%; ADD, 90.6%); 8-14 year olds (CON, 96.8%; ADD 95.9%); and 14-20 year-olds (CON, 100.0%; ADD, 97.1%). The connectome manifested decreased and increased coherence. Patterns were complex and bi-hemispheric; typically reported front-back and left-right loading patterns were not observed. Subtemporal electrodes (seldom utilized) were prominently involved.  CONCLUSIONS: Results demonstrate a stable coherence connectome differentiating ADD from CON subjects including subgroups with and without comorbidities and/or medications. This functional 'connectome', constitutes a diagnostic ADD phenotype. Split-half replications support potential for EEG-based ADD diagnosis, with increased accuracy using limited age ranges. Repeated studies could assist recognition of physiological change from interventions (pharmacological, behavioral).

Mortality in Patients with Myalgic Encephalomyelitis and Chronic Fatigue Syndrome

BACKGROUND

There is a dearth of research examining mortality in individuals with myalgic encephalomyelitis (ME) and chronic fatigue syndrome (CFS). Some studies suggest there is an elevated risk of suicide and earlier mortality compared to national norms. However, findings are inconsistent as other researchers have not found significant increases in all-cause mortality for patients.

OBJECTIVE

This study sought to determine if patients with ME or CFS are reportedly dying earlier than the overall population from the same cause.

METHODS

Family, friends, and caregivers of deceased individuals with ME or CFS were recruited through social media, patient newsletters, emails, and advocate websites. This study analyzed data including cause and age of death for 56 individuals identified as having ME or CFS.

RESULTS

The findings suggest patients in this sample are at a significantly increased risk of earlier all-cause (M = 55.9 years) and cardiovascular-related (M = 58.8 years) mortality, and they had a directionally lower mean age of death for suicide (M = 41.3 years) and cancer (M =66.3 years) compared to the overall U.S. population [M = 73.5 (all-cause), 77.7 (cardiovascular), 47.4 (suicide), and 71.1 (cancer) years of age].

CONCLUSIONS

The results suggest there is an increase in risk for earlier mortality in patients with ME and CFS. Due to the small sample size and over-representation of severely ill patients, the findings should be replicated to determine if the directional differences for suicide and cancer mortality are significantly different from the overall U.S. population.

A Pair of Identical Twins Discordant for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Differ in Physiological Parameters and Gut Microbiome Composition

BACKGROUND Patients with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) present with profound fatigue, flu-like symptoms, pain, cognitive impairment, orthostatic intolerance, and post-exertional malaise (PEM), and exacerbation of some or all of the baseline symptoms. CASE REPORT We report on a pair of 34-year-old monozygotic twins discordant for ME/CFS, with WELL, the non-affected twin, and ILL, the affected twin. Both twins performed a two-day cardiopulmonary exercise test (CPET), pre- and post-exercise blood samples were drawn, and both provided stool samples for biochemical and molecular analysis. At peak exertion for both CPETs, ILL presented lower VO2peak and peak workload compared to WELL. WELL demonstrated normal reproducibility of VO2@ventilatory/anaerobic threshold (VAT) during  CPET2, whereas ILL experienced an abnormal reduction of 13% in VAT during  CPET2. A normal rise in lactate dehydrogenase (LDH), creatine kinase (CK), adrenocorticotropic hormone (ACTH), cortisol, creatinine, and ferritin content was observed following exercise for both WELL and ILL at each CPET. ILL showed higher increases of resistin, soluble CD40 ligand (sCD40L), and soluble Fas ligand (sFasL) after exercise compared to WELL. The gut bacterial microbiome and virome were examined and revealed a lower microbial diversity in ILL compared to WELL, with fewer beneficial bacteria such as Faecalibacterium and Bifidobacterium, and an expansion of bacteriophages belonging to the tailed dsDNA Caudovirales order.  CONCLUSIONS Results suggest dysfunctional immune activation in ILL following exercise and that prokaryotic viruses may contribute to mucosal inflammation and bacterial dysbiosis. Therefore, a two-day CPET and molecular analysis of blood and microbiomes could provide valuable information about ME/CFS, particularly if applied to a larger cohort of monozygotic twins.

Electroencephalogram characteristics in patients with chronic fatigue syndrome

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.

Neurophysiological differences between patients clinically at high risk for schizophrenia and neurotypical controls--first steps in development of a biomarker

BACKGROUND

Schizophrenia is a severe, disabling and prevalent mental disorder without cure and with a variable, incomplete pharmacotherapeutic response. Prior to onset in adolescence or young adulthood a prodromal period of abnormal symptoms lasting weeks to years has been identified and operationalized as clinically high risk (CHR) for schizophrenia. However, only a minority of subjects prospectively identified with CHR convert to schizophrenia, thereby limiting enthusiasm for early intervention(s). This study utilized objective resting electroencephalogram (EEG) quantification to determine whether CHR constitutes a cohesive entity and an evoked potential to assess CHR cortical auditory processing.

METHODS

This study constitutes an EEG-based quantitative neurophysiological comparison between two unmedicated subject groups: 35 neurotypical controls (CON) and 22 CHR patients. After artifact management, principal component analysis (PCA) identified EEG spectral and spectral coherence factors described by associated loading patterns. Discriminant function analysis (DFA) determined factors' discrimination success between subjects in the CON and CHR groups. Loading patterns on DFA-selected factors described CHR-specific spectral and coherence differences when compared to controls. The frequency modulated auditory evoked response (FMAER) explored functional CON-CHR differences within the superior temporal gyri.

RESULTS

Variable reduction by PCA identified 40 coherence-based factors explaining 77.8% of the total variance and 40 spectral factors explaining 95.9% of the variance. DFA demonstrated significant CON-CHR group difference (P <0.00001) and successful jackknifed subject classification (CON, 85.7%; CHR, 86.4% correct). The population distribution plotted along the canonical discriminant variable was clearly bimodal. Coherence factors delineated loading patterns of altered connectivity primarily involving the bilateral posterior temporal electrodes. However, FMAER analysis showed no CON-CHR group differences.

CONCLUSIONS

CHR subjects form a cohesive group, significantly separable from CON subjects by EEG-derived indices. Symptoms of CHR may relate to altered connectivity with the posterior temporal regions but not to primary auditory processing abnormalities within these regions.

Accurate diagnosis of myalgic encephalomyelitis and chronic fatigue syndrome based upon objective test methods for characteristic symptoms

Although myalgic encephalomyelitis (ME) and chronic fatigue syndrome (CFS) are considered to be synonymous, the definitional criteria for ME and CFS define two distinct, partially overlapping, clinical entities. ME, whether defined by the original criteria or by the recently proposed criteria, is not equivalent to CFS, let alone a severe variant of incapacitating chronic fatigue. Distinctive features of ME are: muscle weakness and easy muscle fatigability, cognitive impairment, circulatory deficits, a marked variability of the symptoms in presence and severity, but above all, post-exertional “malaise”: a (delayed) prolonged aggravation of symptoms after a minor exertion. In contrast, CFS is primarily defined by (unexplained) chronic fatigue, which should be accompanied by four out of a list of 8 symptoms, e.g., headaches. Due to the subjective nature of several symptoms of ME and CFS, researchers and clinicians have questioned the physiological origin of these symptoms and qualified ME and CFS as functional somatic syndromes. However, various characteristic symptoms, e.g., post-exertional “malaise” and muscle weakness, can be assessed objectively using well-accepted methods, e.g., cardiopulmonary exercise tests and cognitive tests. The objective measures acquired by these methods should be used to accurately diagnose patients, to evaluate the severity and impact of the illness objectively and to assess the positive and negative effects of proposed therapies impartially.

Evidence in chronic fatigue syndrome for severity-dependent upregulation of prefrontal myelination that is independent of anxiety and depression

White matter (WM) involvement in chronic fatigue syndrome (CFS) was assessed using voxel-based regressions of brain MRI against CFS severity scores and CFS duration in 25 subjects with CFS and 25 normal controls (NCs). As well as voxel-based morphometry, a novel voxel-based quantitative analysis of T1 - and T2 -weighted spin-echo (T1w and T2w) MRI signal level was performed. Severity scores included the Bell CFS disability scale and scores based on the 10 most common CFS symptoms. Hospital Anxiety and Depression Scale (HADS) depression and anxiety scores were included as nuisance covariates. By relaxing the threshold for cluster formation, we showed that the T1w signal is elevated with increasing CFS severity in the ventrolateral thalamus, internal capsule and prefrontal WM. Earlier reports of WM volume losses and neuroinflammation in the midbrain, together with the upregulated prefrontal myelination suggested here, are consistent with the midbrain changes being associated with impaired nerve conduction which stimulates a plastic response on the cortical side of the thalamic relay in the same circuits. The T2w signal versus CFS duration and comparison of T2w signal in the CFS group with the NC group revealed changes in the right middle temporal lobe WM, where impaired communication can affect cognitive function. Adjustment for depression markedly strengthened cluster statistics and increased cluster size in both T1w severity regressions, but adjustment for anxiety less so. Thus, depression and anxiety are statistical confounders here, meaning that they contribute variance to the T1w signal in prefrontal WM but this does not correlate with the co-located variance from CFS severity. MRI regressions with depression itself only detected associations with WM volume, also located in prefrontal WM. We propose that impaired reciprocal brain-body and brain-brain communication through the midbrain provokes peripheral and central responses which contribute to CFS symptoms. Although anxiety, depression and CFS may share biological features, the present evidence indicates that CFS is a distinct disorder.

Myalgic Encephalomyelitis: Symptoms and Biomarkers

Myalgic Encephalomyelitis (ME) continues to cause significant morbidity worldwide with an estimated one million cases in the United States. Hurdles to establishing consensus to achieve accurate evaluation of patients with ME continue, fueled by poor agreement about case definitions, slow progress in development of standardized diagnostic approaches, and issues surrounding research priorities. Because there are other medical problems, such as early MS and Parkinson's Disease, which have some similar clinical presentations, it is critical to accurately diagnose ME to make a differential diagnosis. In this article, we explore and summarize advances in the physiological and neurological approaches to understanding, diagnosing, and treating ME. We identify key areas and approaches to elucidate the core and secondary symptom clusters in ME so as to provide some practical suggestions in evaluation of ME for clinicians and researchers. This review, therefore, represents a synthesis of key discussions in the literature, and has important implications for a better understanding of ME, its biological markers, and diagnostic criteria. There is a clear need for more longitudinal studies in this area with larger data sets, which correct for multiple testing.

Metabolic therapy with Deanna Protocol supplementation delays disease progression and extends survival in amyotrophic lateral sclerosis (ALS) mouse model

Amyotrophic Lateral Sclerosis (ALS), also known as Lou Gehrig's disease, is a neurodegenerative disorder of motor neurons causing progressive muscle weakness, paralysis, and eventual death from respiratory failure. There is currently no cure or effective treatment for ALS. Besides motor neuron degeneration, ALS is associated with impaired energy metabolism, which is pathophysiologically linked to mitochondrial dysfunction and glutamate excitotoxicity. The Deanna Protocol (DP) is a metabolic therapy that has been reported to alleviate symptoms in patients with ALS. In this study we hypothesized that alternative fuels in the form of TCA cycle intermediates, specifically arginine-alpha-ketoglutarate (AAKG), the main ingredient of the DP, and the ketogenic diet (KD), would increase motor function and survival in a mouse model of ALS (SOD1-G93A). ALS mice were fed standard rodent diet (SD), KD, or either diets containing a metabolic therapy of the primary ingredients of the DP consisting of AAKG, gamma-aminobutyric acid, Coenzyme Q10, and medium chain triglyceride high in caprylic triglyceride. Assessment of ALS-like pathology was performed using a pre-defined criteria for neurological score, accelerated rotarod test, paw grip endurance test, and grip strength test. Blood glucose, blood beta-hydroxybutyrate, and body weight were also monitored. SD+DP-fed mice exhibited improved neurological score from age 116 to 136 days compared to control mice. KD-fed mice exhibited better motor performance on all motor function tests at 15 and 16 weeks of age compared to controls. SD+DP and KD+DP therapies significantly extended survival time of SOD1-G93A mice by 7.5% (p = 0.001) and 4.2% (p = 0.006), respectively. Sixty-three percent of mice in the KD+DP and 72.7% of the SD+DP group lived past 125 days, while only 9% of the control animals survived past that point. Targeting energy metabolism with metabolic therapy produces a therapeutic effect in ALS mice which may prolong survival and quality of life in ALS patients.

The relationship of Asperger's syndrome to autism: a preliminary EEG coherence study

BACKGROUND

It has long been debated whether Asperger's Syndrome (ASP) should be considered part of the Autism Spectrum Disorders (ASD) or whether it constitutes a unique entity. The Diagnostic and Statistical Manual, fourth edition (DSM-IV) differentiated ASP from high functioning autism. However, the new DSM-5 umbrellas ASP within ASD, thus eliminating the ASP diagnosis. To date, no clear biomarkers have reliably distinguished ASP and ASD populations. This study uses EEG coherence, a measure of brain connectivity, to explore possible neurophysiological differences between ASP and ASD.

METHODS

Voluminous coherence data derived from all possible electrode pairs and frequencies were previously reduced by principal components analysis (PCA) to produce a smaller number of unbiased, data-driven coherence factors. In a previous study, these factors significantly and reliably differentiated neurotypical controls from ASD subjects by discriminant function analysis (DFA). These previous DFA rules are now applied to an ASP population to determine if ASP subjects classify as control or ASD subjects. Additionally, a new set of coherence based DFA rules are used to determine whether ASP and ASD subjects can be differentiated from each other.

RESULTS

Using prior EEG coherence based DFA rules that successfully classified subjects as either controls or ASD, 96.2% of ASP subjects are classified as ASD. However, when ASP subjects are directly compared to ASD subjects using new DFA rules, 92.3% ASP subjects are identified as separate from the ASD population. By contrast, five randomly selected subsamples of ASD subjects fail to reach significance when compared to the remaining ASD populations. When represented by the discriminant variable, both the ASD and ASD populations are normally distributed.

CONCLUSIONS

Within a control-ASD dichotomy, an ASP population falls closer to ASD than controls. However, when compared directly with ASD, an ASP population is distinctly separate. The ASP population appears to constitute a neurophysiologically identifiable, normally distributed entity within the higher functioning tail of the ASD population distribution. These results must be replicated with a larger sample given their potentially immense clinical, emotional and financial implications for affected individuals, their families and their caregivers.

Effect of oxcarbazepine on background EEG activity and cognition in epilepsy

BACKGROUND AND PURPOSE

Cognitive dysfunction related to antiepileptic drugs (AEDs) is an important issue in the management of patients with epilepsy. The aim of the present study was to evaluate relative long-term effects of oxcarbazepine (OXC) on cognition in drug-naive patients with epilepsy.

METHODS

Fifteen drug-naïve epilepsy patients were enrolled. Electroencephalogram (EEG) recordings and neuropsychological (NP) tests were performed before and after OXC monotherapy. The relative power of the discrete frequency bandwas obtained. In addition, interhemispheric and intrahemispheric spectral coherence was also calculated.

RESULTS

NP tests showed significant improvement in visuo-spatial, memory and executive function after OXC treatment. However, neither spectral power nor coherence changed significantly with OXC treatment.

CONCLUSIONS

Our study supports the notion that OXC has no significant cognitive side effect in patients with epilepsy.

A machine learning approach using EEG data to predict response to SSRI treatment for major depressive disorder

OBJECTIVE

The problem of identifying, in advance, the most effective treatment agent for various psychiatric conditions remains an elusive goal. To address this challenge, we investigate the performance of the proposed machine learning (ML) methodology (based on the pre-treatment electroencephalogram (EEG)) for prediction of response to treatment with a selective serotonin reuptake inhibitor (SSRI) medication in subjects suffering from major depressive disorder (MDD).

METHODS

A relatively small number of most discriminating features are selected from a large group of candidate features extracted from the subject's pre-treatment EEG, using a machine learning procedure for feature selection. The selected features are fed into a classifier, which was realized as a mixture of factor analysis (MFA) model, whose output is the predicted response in the form of a likelihood value. This likelihood indicates the extent to which the subject belongs to the responder vs. non-responder classes. The overall method was evaluated using a "leave-n-out" randomized permutation cross-validation procedure.

RESULTS

A list of discriminating EEG biomarkers (features) was found. The specificity of the proposed method is 80.9% while sensitivity is 94.9%, for an overall prediction accuracy of 87.9%. There is a 98.76% confidence that the estimated prediction rate is within the interval [75%, 100%].

CONCLUSIONS

These results indicate that the proposed ML method holds considerable promise in predicting the efficacy of SSRI antidepressant therapy for MDD, based on a simple and cost-effective pre-treatment EEG.

SIGNIFICANCE

The proposed approach offers the potential to improve the treatment of major depression and to reduce health care costs.

The neuropsychiatric and neuropsychological features of chronic fatigue syndrome: revisiting the enigma

The aim of this article is to provide a comprehensive and updated review of the key neuropsychiatric and neuropsychological complaints associated with chronic fatigue syndrome (CFS). Neuropsychiatric and neuropsychological difficulties are common in CFS and are linked primarily to disorders of mood, affect and behaviour. The neuropsychiatric complaint most frequently encountered amongst CFS patients is depression and in particular major depressive disorder (MDD). Despite decades of research, the precise aetiological relationship between CFS and MDD remains poorly understood. This has resulted in the development of a number of interesting and polarised hypotheses regarding the aetiological nature of CFS. Recent scientific advances have however begun to unravel a number of interesting inflammatory and immunological explanations that suggest CFS and MDD are distinct yet interrelated conditions. The possibility that the overlap between CFS and MDD might be explained in terms of shared oxidative and nitrosative (IO&NS) pathways is an area of intense research interest and is reviewed in detail in this article. The overlap between CFS and MDD is further differentiated by variations in HPA axis activity between the two disorders. Important immunological differences between MDD and CFS are also reviewed with particular emphasis on antiviral RNase L pathways in CFS. In addition to the presence of neuropsychiatric complaints, CFS is also associated with neuropsychological symptoms such as impaired attention, memory and reaction time. The key neuropsychological problems reported by CFS patients are also included in the review in an effort to understand the significance of cognitive impairment in CFS.

A stable pattern of EEG spectral coherence distinguishes children with autism from neuro-typical controls - a large case control study

BACKGROUND

The autism rate has recently increased to 1 in 100 children. Genetic studies demonstrate poorly understood complexity. Environmental factors apparently also play a role. Magnetic resonance imaging (MRI) studies demonstrate increased brain sizes and altered connectivity. Electroencephalogram (EEG) coherence studies confirm connectivity changes. However, genetic-, MRI- and/or EEG-based diagnostic tests are not yet available. The varied study results likely reflect methodological and population differences, small samples and, for EEG, lack of attention to group-specific artifact.

METHODS

Of the 1,304 subjects who participated in this study, with ages ranging from 1 to 18 years old and assessed with comparable EEG studies, 463 children were diagnosed with autism spectrum disorder (ASD); 571 children were neuro-typical controls (C). After artifact management, principal components analysis (PCA) identified EEG spectral coherence factors with corresponding loading patterns. The 2- to 12-year-old subsample consisted of 430 ASD- and 554 C-group subjects (n = 984). Discriminant function analysis (DFA) determined the spectral coherence factors' discrimination success for the two groups. Loading patterns on the DFA-selected coherence factors described ASD-specific coherence differences when compared to controls.

RESULTS

Total sample PCA of coherence data identified 40 factors which explained 50.8% of the total population variance. For the 2- to 12-year-olds, the 40 factors showed highly significant group differences (P < 0.0001). Ten randomly generated split half replications demonstrated high-average classification success (C, 88.5%; ASD, 86.0%). Still higher success was obtained in the more restricted age sub-samples using the jackknifing technique: 2- to 4-year-olds (C, 90.6%; ASD, 98.1%); 4- to 6-year-olds (C, 90.9%; ASD 99.1%); and 6- to 12-year-olds (C, 98.7%; ASD, 93.9%). Coherence loadings demonstrated reduced short-distance and reduced, as well as increased, long-distance coherences for the ASD-groups, when compared to the controls. Average spectral loading per factor was wide (10.1 Hz).

CONCLUSIONS

Classification success suggests a stable coherence loading pattern that differentiates ASD- from C-group subjects. This might constitute an EEG coherence-based phenotype of childhood autism. The predominantly reduced short-distance coherences may indicate poor local network function. The increased long-distance coherences may represent compensatory processes or reduced neural pruning. The wide average spectral range of factor loadings may suggest over-damped neural networks.