Electroencephalography (EEG) produces time-series data of neural oscillations in the brain, and is one of the most commonly used methods for investigating both normal brain functions and brain disorders. Quantitative EEG analysis enables identification of frequencies and brain activity that are activated or impaired. With studies on the structural and functional networks of the brain, the concept of the brain as a complex network has been fundamental to understand normal brain functions and the pathophysiology of various neurological disorders. Functional connectivity is a measure of neural synchrony in the brain network that refers to the statistical interdependency between neural oscillations over time. In this review, we first discuss the basic methods of EEG analysis, including preprocessing, spectral analysis, and functional-connectivity and graph-theory measures. We then review previous EEG studies of brain network characterization in several neurological disorders, including epilepsy, Alzheimer’s disease, dementia with Lewy bodies, and idiopathic rapid eye movement sleep behavior disorder. Identifying the EEG-based network characteristics might improve the understanding of disease processes and aid the development of novel therapeutic approaches for various neurological disorders.

Citations

• Augmented Recognition of Distracted Driving State Based on Electrophysiological Analysis of Brain Network
  Geqi Qi, Rui Liu, Wei Guan, Ailing Huang
  Cyborg and Bionic Systems.2024;[Epub]     CrossRef

Detection of underling proteinopathies is becoming increasingly important across neurodegenerative conditions due to upcoming disease intervention trials. In this review, we explored how temporal lobe changes in amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) can potentially predict underlying TDP-43 pathology subtypes in FTD. To date, emphasis has been given to frontal lobe changes in the study of the cognitive and behavioural impairments in both syndromes but an increasing number of pathological, imaging and neuropsychological studies suggest how temporal lobe changes could critically affect the cognition and behaviour of these conditions. In this current article, we reviewed pathological, imaging as well as clinical/neuropsychological findings of temporal involvement in the ALS-FTD continuum, how they relate to temporal lobe changes and the underlying TDP-43 pathology in FTD. Findings across studies show that TDP-43 pathology occurs and coincides in many structures in ALS and FTD, but especially in the temporal lobes. In particular, anterior and medial temporal lobes atrophy is consistently found in ALS and FTD. In addition, memory and language impairment as well as emotional and Theory of Mind processing deficits that are characteristics of the two diseases are highly correlated to temporal lobe dysfunction. We conclude by showing that temporal lobe changes due to TDP-43 type B might be particular predictive of TDP-43 type B pathology in behavioural variant FTD, which clearly needs to be investigated further in the future.

Citations

• A comparative study of cognitive and behavioral profiles between sporadic and type 8 amyotrophic lateral sclerosis
  Cássia de Alcântara, Marcelo Maroco Cruzeiro, Marcondes Cavalcante França, Mariana Asmar Alencar, Antônio Jaeger, Caroline Martins de Araújo, Natália Araújo Sundfeld da Gama, Sarah Teixeira Camargos, Leonardo Cruz de Souza
  Muscle & Nerve.2023; 68(3): 316.     CrossRef
• In vivo exploration of synaptic projections in frontotemporal dementia
  Eric Salmon, Mohamed Ali Bahri, Alain Plenevaux, Guillaume Becker, Alain Seret, Emma Delhaye, Christian Degueldre, Evelyne Balteau, Christian Lemaire, André Luxen, Christine Bastin
  Scientific Reports.2021;[Epub]     CrossRef
• Structural and functional papez circuit integrity in amyotrophic lateral sclerosis
  Ana Paula Arantes Bueno, Walter Hugo L. Pinaya, Luciana M. Moura, Maxime Bertoux, Ratko Radakovic, Matthew C. Kiernan, Antonio Lucio Teixeira, Leonardo Cruz de Souza, Michael Hornberger, João Ricardo Sato
  Brain Imaging and Behavior.2018; 12(6): 1622.     CrossRef