The Use of Quantitative EEG for Differentiating Frontotemporal Dementia From Late-Onset Bipolar Disorder

Social and non-social working memory in neurodegeneration

Although social functioning relies on working memory, whether a social-specific mechanism exists remains unclear. This undermines the characterization of neurodegenerative conditions with both working memory and social deficits. We assessed working memory domain-specificity across behavioral, electrophysiological, and neuroimaging dimensions in 245 participants. A novel working memory task involving social and non-social stimuli with three load levels was assessed across controls and different neurodegenerative conditions with recognized impairments in: working memory and social cognition (behavioral-variant frontotemporal dementia); general cognition (Alzheimer's disease); and unspecific patterns (Parkinson's disease). We also examined resting-state theta oscillations and functional connectivity correlates of working memory domain-specificity. Results in controls and all groups together evidenced increased working memory demands for social stimuli associated with frontocinguloparietal theta oscillations and salience network connectivity. Canonical frontal theta oscillations and executive-default mode network anticorrelation indexed non-social stimuli. Behavioral-variant frontotemporal dementia presented generalized working memory deficits related to posterior theta oscillations, with social stimuli linked to salience network connectivity. In Alzheimer's disease, generalized working memory impairments were related to temporoparietal theta oscillations, with non-social stimuli linked to the executive network. Parkinson's disease showed spared working memory performance and canonical brain correlates. Findings support a social-specific working memory and related disease-selective pathophysiological mechanisms.

Bipolar disorder and frontotemporal dementia: A systematic review

OBJECTIVES

To detail the biological, clinical and neurocognitive characteristics differentiating bipolar disorder (BD) from frontotemporal dementia (FTD) and to investigate whether BD is a risk factor for FTD.

METHODS

A total of 16 studies were included in this systematic review. Five studies described biological and/or neurocognitive characteristics between patients with BD and FTD, and 11 studies investigated whether BD was a risk factor for FTD.

RESULTS

Individuals with FTD presented higher levels of serum neurofilament light chain, greater grey matter reduction in frontal, parietal and temporal lobes, and increased slow wave oscillations in channels F3, F4, T3, T5, T4 and T6 within an electroencephalogram (EEG), relative to individuals with BD. Patients with FTD presented greater deficits in executive function and theory of mind compared to patients with BD in a euthymic state, and more deficits in verbal fluency compared to patients with BD in a current mood episode. Patients with BD in a current mood episode showed greater impairment in attention, working memory, verbal memory and executive function relative to individuals with FTD. In addition, retrospective studies showed that 10.2%-11.6% of patients with behavioural variant FTD (bvFTD) had a preceding history of BD.

CONCLUSION

Biological and neurocognitive characteristics help to distinguish between BD and FTD, and it may help to reach a more precise diagnosis. In addition, individuals with BD are at higher risk of developing FTD. More studies are needed to identify the predictors of the conversion between BD to FTD.

Application of Machine Learning to Electroencephalography for the Diagnosis of Primary Progressive Aphasia: A Pilot Study

Background. Primary progressive aphasia (PPA) is a neurodegenerative syndrome in which diagnosis is usually challenging. Biomarkers are needed for diagnosis and monitoring. In this study, we aimed to evaluate Electroencephalography (EEG) as a biomarker for the diagnosis of PPA. Methods. We conducted a cross-sectional study with 40 PPA patients categorized as non-fluent, semantic, and logopenic variants, and 20 controls. Resting-state EEG with 32 channels was acquired and preprocessed using several procedures (quantitative EEG, wavelet transformation, autoencoders, and graph theory analysis). Seven machine learning algorithms were evaluated (Decision Tree, Elastic Net, Support Vector Machines, Random Forest, K-Nearest Neighbors, Gaussian Naive Bayes, and Multinomial Naive Bayes). Results. Diagnostic capacity to distinguish between PPA and controls was high (accuracy 75%, F1-score 83% for kNN algorithm). The most important features in the classification were derived from network analysis based on graph theory. Conversely, discrimination between PPA variants was lower (Accuracy 58% and F1-score 60% for kNN). Conclusions. The application of ML to resting-state EEG may have a role in the diagnosis of PPA, especially in the differentiation from controls. Future studies with high-density EEG should explore the capacity to distinguish between PPA variants.

EEG based Major Depressive disorder and Bipolar disorder detection using Neural Networks:A review

Mental disorders represent critical public health challenges as they are leading contributors to the global burden of disease and intensely influence social and financial welfare of individuals. The present comprehensive review concentrate on the two mental disorders: Major depressive Disorder (MDD) and Bipolar Disorder (BD) with noteworthy publications during the last ten years. There is a big need nowadays for phenotypic characterization of psychiatric disorders with biomarkers. Electroencephalography (EEG) signals could offer a rich signature for MDD and BD and then they could improve understanding of pathophysiological mechanisms underling these mental disorders. In this review, we focus on the literature works adopting neural networks fed by EEG signals. Among those studies using EEG and neural networks, we have discussed a variety of EEG based protocols, biomarkers and public datasets for depression and bipolar disorder detection. We conclude with a discussion and valuable recommendations that will help to improve the reliability of developed models and for more accurate and more deterministic computational intelligence based systems in psychiatry. This review will prove to be a structured and valuable initial point for the researchers working on depression and bipolar disorders recognition by using EEG signals.

Behavioral variant frontotemporal dementia in patients with previous severe mental illness: a systematic and critical review

ABSTRACT Objectives: To explore the relationship between severe/serious mental illness (SMI) and the behavioral variant of frontotemporal dementia (bvFTD), as the patterns of symptoms and cognitive performance that characterize both disorders share similarities. Methods: We performed a systematic review investigating what has already been published regarding the relationship between bvFTD and SMI. Studies were selected from PubMed and LILACS databases, including those published up to February 12, 2018. The search strategy included the following terms: “frontotemporal dementia” plus “bipolar”, OR “frontotemporal dementia” plus “schizophrenia”, OR “frontotemporal dementia” plus “schizoaffective”. Publications without abstracts, case reports with absent genetic or histopathological confirmation, reviews and non-English language papers were excluded across the search process. Results: The search on PubMed retrieved 186 articles, of which 42 met eligibility criteria. On the LILACS database, none met the requirements. Generally, three major research aims were identified: 1) to look for frontotemporal lobar degeneration-associated genetic abnormalities in patients with prior SMI; 2) to compare the cognitive profile between patients affected by neurodegenerative disorders and schizophrenic patients; 3) to highlight the association between bvFTD and preceding psychiatric conditions and/or distinguish them both. The investigated mutations were found infrequently in the studied SMI samples. Cross-sectional studies comparing cognitive performance between bvFTD and psychiatric disorders mostly found no remarkable differences. There were only a few case reports identifying definite frontotemporal lobar degeneration in patients with previous psychiatric diagnoses. Conclusions: The available evidence demonstrates how fragile the current understanding is regarding the association between bvFTD and prior SMI.

Measuring network disruption in neurodegenerative diseases: New approaches using signal analysis

Advanced neuroimaging has increased understanding of the pathogenesis and spread of disease, and offered new therapeutic targets. MRI and positron emission tomography have shown that neurodegenerative diseases including Alzheimer's disease (AD), Lewy body dementia (LBD), Parkinson's disease (PD), frontotemporal dementia (FTD), amyotrophic lateral sclerosis (ALS) and multiple sclerosis (MS) are associated with changes in brain networks. However, the underlying neurophysiological pathways driving pathological processes are poorly defined. The gap between what imaging can discern and underlying pathophysiology can now be addressed by advanced techniques that explore the cortical neural synchronisation, excitability and functional connectivity that underpin cognitive, motor, sensory and other functions. Transcranial magnetic stimulation can show changes in focal excitability in cortical and transcortical motor circuits, while electroencephalography and magnetoencephalography can now record cortical neural synchronisation and connectivity with good temporal and spatial resolution.Here we reflect on the most promising new approaches to measuring network disruption in AD, LBD, PD, FTD, MS, and ALS. We consider the most groundbreaking and clinically promising studies in this field. We outline the limitations of these techniques and how they can be tackled and discuss how these novel approaches can assist in clinical trials by predicting and monitoring progression of neurophysiological changes underpinning clinical symptomatology.