TY - GEN
T1 - Resting-state Gamma-band EEG Abnormalities in Autism∗
AU - Shou, Guofa
AU - Mosconi, Matthew W.
AU - Ethridge, Lauren E.
AU - Sweeney, John A.
AU - Ding, Lei
N1 - Funding Information:
*NSF CAREER ECCS-0955260 (LD), NSF RII Track-2 FEC 1539068 (LD) U54HD082008 (JS, MM, LE) and the National Institute of Mental Health Grant 112734 (MM).
Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/26
Y1 - 2018/10/26
N2 - Gamma-band rhythmic abnormalities have been of significant interests in autism spectrum disorders (ASD). Most studies used magnetoencephalography (MEG) due to its advantage in measuring weak gamma signals as compared to electroencephalography (EEG). However, EEG is more accessible, portable, and importantly, more sensitive to cortical sources located at the crowns of gyri, than MEG. Therefore, it is extremely valuable if EEG can be used to detect gamma-band abnormalities in ASD, which could provide complementary insights on pathology of ASD. One challenge in detecting gamma-band neural activities is to remove muscular artifacts, which share the same frequency band. In the present study, we used a previously developed time-frequency independent component analysis (ICA)approach to probe EEG gamma-band abnormalities in ASD. We examined functional connectivity (FC) patterns on intrinsic connectivity networks (ICNs), i.e., the ICs representing distributed neural activities obtained from ICA, using the metrics of spectral power of individual ICNs and coherence between different ICNs. Seven ICNs that reassembled ICNs obtained from EEG data in the band of 2-30 Hz, were successfully identified in the gamma-band (31-50 Hz) data by the approach. Local over-connectivity in the bilateral frontal and left parietal ICNs, as well as long-range under-connectivity between left and right motor ICNs, were observed in ASD. In addition, the age-related effect was identified in the left motor and left parietal ICNs in healthy control, but not in ASD. These findings demonstrated a mixed pattern of gamma-band FC changes in ASD. It further indicated that the developed approach is promising in reconstructing gamma-band patterns from resting-state EEG signals.
AB - Gamma-band rhythmic abnormalities have been of significant interests in autism spectrum disorders (ASD). Most studies used magnetoencephalography (MEG) due to its advantage in measuring weak gamma signals as compared to electroencephalography (EEG). However, EEG is more accessible, portable, and importantly, more sensitive to cortical sources located at the crowns of gyri, than MEG. Therefore, it is extremely valuable if EEG can be used to detect gamma-band abnormalities in ASD, which could provide complementary insights on pathology of ASD. One challenge in detecting gamma-band neural activities is to remove muscular artifacts, which share the same frequency band. In the present study, we used a previously developed time-frequency independent component analysis (ICA)approach to probe EEG gamma-band abnormalities in ASD. We examined functional connectivity (FC) patterns on intrinsic connectivity networks (ICNs), i.e., the ICs representing distributed neural activities obtained from ICA, using the metrics of spectral power of individual ICNs and coherence between different ICNs. Seven ICNs that reassembled ICNs obtained from EEG data in the band of 2-30 Hz, were successfully identified in the gamma-band (31-50 Hz) data by the approach. Local over-connectivity in the bilateral frontal and left parietal ICNs, as well as long-range under-connectivity between left and right motor ICNs, were observed in ASD. In addition, the age-related effect was identified in the left motor and left parietal ICNs in healthy control, but not in ASD. These findings demonstrated a mixed pattern of gamma-band FC changes in ASD. It further indicated that the developed approach is promising in reconstructing gamma-band patterns from resting-state EEG signals.
UR - http://www.scopus.com/inward/record.url?scp=85056632640&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85056632640&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2018.8512718
DO - 10.1109/EMBC.2018.8512718
M3 - Conference contribution
C2 - 30440772
AN - SCOPUS:85056632640
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 1915
EP - 1918
BT - 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
Y2 - 18 July 2018 through 21 July 2018
ER -