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BEGIN:VEVENT
UID:20260520T211017Z - 54757@eupp247
DTSTART;TZID=Europe/Berlin:20220316T160000
DTEND;TZID=Europe/Berlin:20220316T170000
CREATED:20260520T211017Z
DESCRIPTION:<a href="https://academy.ant-neuro.com/event/neurorehabilitatio
 n-for-parkinsons-disease-can-remote-rseeg-be-used-as-a-valid-predictive-me
 asure-of-short-term-plasticity-17/register">Neurorehabilitation for Parkin
 son’s disease: Can remote rsEEG be used as a valid predictive measure of
  short term plasticity?</a>\nThis webinar has ended. You can watch the rec
 orded session here. Welcome to ANT Neuro Educational Webinar Series! Parki
 nson's disease (PD) is a progressive neurodegenerative disorder with no cu
 re and few treatments can alter its progression. Exercise with dance and m
 usic\, with virtual or onsite participation\, improves motor (Hackney et a
 l 2006)\, and nonmotor symptoms (Westheimer 2008) as well as mood (Fontane
 si & DeSouza 2021\; Ghanai et al 2021). Preliminary evidence from our lab 
 has revealed a slowing of typical PD progression related to dance class pa
 rticipation (Bearss & DeSouza\, 2021). Utilizing modelling of behavioral a
 nd neuroimaging data we will now display rsEEG alpha rhythm brain changes 
 after participation in a single 75-minute dance class using the EMOTIV EPO
 C 14-channel cap (Levkov 2015\; Bearss 2021). Our longitudinal study sugge
 sted disease motor and nonmotor symptom progression is reduced for weekly 
 dance participants (n = 16 PD) compared to the Michael J. Fox Foundation (
 PPMI) reference data (n = 16) (Bearss & DeSouza 2021). Additionally\, rsEE
 G alpha rhythm change has been observed over the 3-year research study per
 iod. Lastly\, I will touch on a paper examining this PPMI longitudinal dat
 a\, where we provide evidence of which measures are consistently (cross-va
 lidated by five models) most predictive of PD (Leger\, Herbert & DeSouza 2
 020). The key aim of this work is to be able to model our behavioural and 
 rsEEG data to identify biomarkers of neurodegenerative disease. This resea
 rch will be presented by Dr. Joseph DeSouza who is an associate professor 
 of Systems Neuroscience at York University. His specific research interest
 s lie within the realms of converting multisensory signals of vision\, aud
 ition and touch into motor movements of the eyes\, hand & body and how the
 se mechanisms are modulated by various mechanisms of short and long term c
 ognitive [...]
DTSTAMP:20260520T211017Z
LOCATION:Online
SUMMARY:Neurorehabilitation for Parkinson’s disease: Can remote rsEEG be 
 used as a valid predictive measure of short term plasticity?
X-ALT-DESC;FMTTYPE=text/html:<a href="https://academy.ant-neuro.com/event/n
 eurorehabilitation-for-parkinsons-disease-can-remote-rseeg-be-used-as-a-va
 lid-predictive-measure-of-short-term-plasticity-17/register">Neurorehabili
 tation for Parkinson’s disease: Can remote rsEEG be used as a valid pred
 ictive measure of short term plasticity?</a>\nThis webinar has ended. You 
 can watch the recorded session here. Welcome to ANT Neuro Educational Webi
 nar Series! Parkinson's disease (PD) is a progressive neurodegenerative di
 sorder with no cure and few treatments can alter its progression. Exercise
  with dance and music\, with virtual or onsite participation\, improves mo
 tor (Hackney et al 2006)\, and nonmotor symptoms (Westheimer 2008) as well
  as mood (Fontanesi & DeSouza 2021\; Ghanai et al 2021). Preliminary evide
 nce from our lab has revealed a slowing of typical PD progression related 
 to dance class participation (Bearss & DeSouza\, 2021). Utilizing modellin
 g of behavioral and neuroimaging data we will now display rsEEG alpha rhyt
 hm brain changes after participation in a single 75-minute dance class usi
 ng the EMOTIV EPOC 14-channel cap (Levkov 2015\; Bearss 2021). Our longitu
 dinal study suggested disease motor and nonmotor symptom progression is re
 duced for weekly dance participants (n = 16 PD) compared to the Michael J.
  Fox Foundation (PPMI) reference data (n = 16) (Bearss & DeSouza 2021). Ad
 ditionally\, rsEEG alpha rhythm change has been observed over the 3-year r
 esearch study period. Lastly\, I will touch on a paper examining this PPMI
  longitudinal data\, where we provide evidence of which measures are consi
 stently (cross-validated by five models) most predictive of PD (Leger\, He
 rbert & DeSouza 2020). The key aim of this work is to be able to model our
  behavioural and rsEEG data to identify biomarkers of neurodegenerative di
 sease. This research will be presented by Dr. Joseph DeSouza who is an ass
 ociate professor of Systems Neuroscience at York University. His specific 
 research interests lie within the realms of converting multisensory signal
 s of vision\, audition and touch into motor movements of the eyes\, hand &
  body and how these mechanisms are modulated by various mechanisms of shor
 t and long term cognitive [...]
END:VEVENT
END:VCALENDAR