Rene L Utianski1, John N Caviness2, Elisabeth C W van Straaten3, Thomas G Beach4, Brittany N Dugger5, Holly A Shill6, Erika D Driver-Dunckley7, Marwan N Sabbagh6, Shyamal Mehta7, Charles H Adler7, Joseph G Hentz7. 1. Department of Neurology, Mayo Clinic, Rochester, MN, USA. 2. Department of Neurology, Mayo Clinic, Scottsdale, AZ, USA. Electronic address: jcaviness@mayo.edu. 3. Department of Clinical Neurophysiology, VU University Medical Center, Amsterdam, The Netherlands. 4. Banner-Sun Health Research Institute, Sun City, AZ, USA. 5. Institute of Neurodegenerative Diseases, University of California in San Francisco, San Francisco, CA, USA. 6. Department of Neurology, Barrow Neurological Institute, Phoenix, AZ, USA. 7. Department of Neurology, Mayo Clinic, Scottsdale, AZ, USA.
Abstract
OBJECTIVES: To determine what differences exist in graph theory network measures derived from electroencephalography (EEG), between Parkinson's disease (PD) patients who are cognitively normal (PD-CN) and matched healthy controls; and between PD-CN and PD dementia (PD-D). METHODS: EEG recordings were analyzed via graph theory network analysis to quantify changes in global efficiency and local integration. This included minimal spanning tree analysis. T-tests and correlations were used to assess differences between groups and assess the relationship with cognitive performance. RESULTS: Network measures showed increased local integration across all frequency bands between control and PD-CN; in contrast, decreased local integration occurred in PD-D when compared to PD-CN in the alpha1 frequency band. Differences found in PD-MCI mirrored PD-D. Correlations were found between network measures and assessments of global cognitive performance in PD. CONCLUSIONS: Our results reveal distinct patterns of band and network measure type alteration and breakdown for PD, as well as with cognitive decline in PD. SIGNIFICANCE: These patterns suggest specific ways that interaction between cortical areas becomes abnormal and contributes to PD symptoms at various stages. Graph theory analysis by EEG suggests that network alteration and breakdown are robust attributes of PD cortical dysfunction pathophysiology.
OBJECTIVES: To determine what differences exist in graph theory network measures derived from electroencephalography (EEG), between Parkinson's disease (PD) patients who are cognitively normal (PD-CN) and matched healthy controls; and between PD-CN and PD dementia (PD-D). METHODS: EEG recordings were analyzed via graph theory network analysis to quantify changes in global efficiency and local integration. This included minimal spanning tree analysis. T-tests and correlations were used to assess differences between groups and assess the relationship with cognitive performance. RESULTS: Network measures showed increased local integration across all frequency bands between control and PD-CN; in contrast, decreased local integration occurred in PD-D when compared to PD-CN in the alpha1 frequency band. Differences found in PD-MCI mirrored PD-D. Correlations were found between network measures and assessments of global cognitive performance in PD. CONCLUSIONS: Our results reveal distinct patterns of band and network measure type alteration and breakdown for PD, as well as with cognitive decline in PD. SIGNIFICANCE: These patterns suggest specific ways that interaction between cortical areas becomes abnormal and contributes to PD symptoms at various stages. Graph theory analysis by EEG suggests that network alteration and breakdown are robust attributes of PD cortical dysfunction pathophysiology.
Authors: M Hassan; L Chaton; P Benquet; A Delval; C Leroy; L Plomhause; A J H Moonen; A A Duits; A F G Leentjens; V van Kranen-Mastenbroek; L Defebvre; P Derambure; F Wendling; K Dujardin Journal: Neuroimage Clin Date: 2017-03-06 Impact factor: 4.881
Authors: Luis R Peraza; Ruth Cromarty; Xenia Kobeleva; Michael J Firbank; Alison Killen; Sara Graziadio; Alan J Thomas; John T O'Brien; John-Paul Taylor Journal: Sci Rep Date: 2018-03-15 Impact factor: 4.379