Babiloni C

Groppa S

Hardiman O

Kiernan MC

McMackin R

Muthuraman M

Nasseroleslami B

Taylor J-P

\ua9 Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.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\u27s disease (AD), Lewy body dementia (LBD), Parkinson\u27s 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

Newcastle University E-Prints

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

https://eprint.ncl.ac.uk/fulltext.aspx?url=255511/3E2D0F89-79A7-477B-9892-A10883194363.pdf&pub_id=255511

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

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