Manfred G Kitzbichler1, Sheraz Khan2, Santosh Ganesan2, Mark G Vangel3, Martha R Herbert4, Matti S Hämäläinen2, Tal Kenet5. 1. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts; Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, United Kingdom. 2. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts. 3. Massachusetts General Hospital/Masachusetts Institute of Technology General Clinical Research Center, Biomedical Imaging Core, Massachusetts General Hospital, Charlestown, Massachusetts. 4. Pediatric Neurology, Massachusetts General Hospital, Charlestown, Massachusetts. 5. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts. Electronic address: tal@nmr.mgh.harvard.edu.
Abstract
BACKGROUND: Extensive evidence indicates that cortical connectivity patterns are abnormal in autism spectrum disorders (ASD), showing both overconnectivity and underconnectivity. Since, however, studies to date have focused on either spatial or spectral dimensions, but not both simultaneously, much remains unknown about the nature of these abnormalities. In particular, it remains unknown whether abnormal connectivity patterns in ASD are driven by specific frequency bands, by spatial network properties, or by some combination of these factors. METHODS: Magnetoencephalography recordings (15 ASD, 15 control subjects) mapped back onto cortical space were used to study resting state networks in ASD with both spatial and spectral specificity. The data were quantified using graph theoretic metrics. RESULTS: The two major factors that drove the nature of connectivity abnormalities in ASD were the mediating frequency band and whether the network included frontal nodes. These factors determined whether clustering and integration were increased or decreased in cortical resting state networks in ASD. These measures also correlated with abnormalities in the developmental trajectory of resting state networks in ASD. Lastly, these measures correlated with ASD severity in some frequency bands and spatially specific subnetworks. CONCLUSIONS: Our findings suggest that network abnormalities in ASD are widespread, are more likely in subnetworks that include the frontal lobe, and can be opposite in nature depending on the frequency band. These findings thus elucidate seemingly contradictory prior findings of both overconnectivity and underconnectivity in ASD.
BACKGROUND: Extensive evidence indicates that cortical connectivity patterns are abnormal in autism spectrum disorders (ASD), showing both overconnectivity and underconnectivity. Since, however, studies to date have focused on either spatial or spectral dimensions, but not both simultaneously, much remains unknown about the nature of these abnormalities. In particular, it remains unknown whether abnormal connectivity patterns in ASD are driven by specific frequency bands, by spatial network properties, or by some combination of these factors. METHODS: Magnetoencephalography recordings (15 ASD, 15 control subjects) mapped back onto cortical space were used to study resting state networks in ASD with both spatial and spectral specificity. The data were quantified using graph theoretic metrics. RESULTS: The two major factors that drove the nature of connectivity abnormalities in ASD were the mediating frequency band and whether the network included frontal nodes. These factors determined whether clustering and integration were increased or decreased in cortical resting state networks in ASD. These measures also correlated with abnormalities in the developmental trajectory of resting state networks in ASD. Lastly, these measures correlated with ASD severity in some frequency bands and spatially specific subnetworks. CONCLUSIONS: Our findings suggest that network abnormalities in ASD are widespread, are more likely in subnetworks that include the frontal lobe, and can be opposite in nature depending on the frequency band. These findings thus elucidate seemingly contradictory prior findings of both overconnectivity and underconnectivity in ASD.
Authors: Vassilis Tsiaras; Panagiotis G Simos; Roozbeh Rezaie; Bhavin R Sheth; Eleftherios Garyfallidis; Eduardo M Castillo; Andrew C Papanicolaou Journal: Comput Biol Med Date: 2011-05-17 Impact factor: 4.589
Authors: Manfred G Kitzbichler; Richard N A Henson; Marie L Smith; Pradeep J Nathan; Edward T Bullmore Journal: J Neurosci Date: 2011-06-01 Impact factor: 6.167
Authors: Sheraz Khan; Konstantinos Michmizos; Mark Tommerdahl; Santosh Ganesan; Manfred G Kitzbichler; Manuel Zetino; Keri-Lee A Garel; Martha R Herbert; Matti S Hämäläinen; Tal Kenet Journal: Brain Date: 2015-03-12 Impact factor: 13.501
Authors: Fahimeh Mamashli; Samantha Huang; Sheraz Khan; Matti S Hämäläinen; Seppo P Ahlfors; Jyrki Ahveninen Journal: Brain Topogr Date: 2020-05-21 Impact factor: 3.020
Authors: Sheraz Khan; Javeria A Hashmi; Fahimeh Mamashli; Konstantinos Michmizos; Manfred G Kitzbichler; Hari Bharadwaj; Yousra Bekhti; Santosh Ganesan; Keri-Lee A Garel; Susan Whitfield-Gabrieli; Randy L Gollub; Jian Kong; Lucia M Vaina; Kunjan D Rana; Steven M Stufflebeam; Matti S Hämäläinen; Tal Kenet Journal: Neuroimage Date: 2018-02-17 Impact factor: 6.556
Authors: Avniel Singh Ghuman; Rebecca N van den Honert; Theodore J Huppert; Gregory L Wallace; Alex Martin Journal: Biol Psychiatry Cogn Neurosci Neuroimaging Date: 2016-08-02
Authors: David M Simon; Cara R Damiano; Tiffany G Woynaroski; Lisa V Ibañez; Michael Murias; Wendy L Stone; Mark T Wallace; Carissa J Cascio Journal: J Autism Dev Disord Date: 2017-09
Authors: Mark Jaime; Camilla M McMahon; Bridget C Davidson; Lisa C Newell; Peter C Mundy; Heather A Henderson Journal: J Autism Dev Disord Date: 2016-04