TY - JOUR
T1 - Genuine high-order interactions in brain networks and neurodegeneration
AU - Herzog, Rubén
AU - Rosas, Fernando E.
AU - Whelan, Robert
AU - Fittipaldi, Sol
AU - Santamaria-Garcia, Hernando
AU - Cruzat, Josephine
AU - Birba, Agustina
AU - Moguilner, Sebastian
AU - Tagliazucchi, Enzo
AU - Prado, Pavel
AU - Ibanez, Agustin
N1 - Funding Information:
AI is supported by Takeda Grant CW2680521 ; CONICET; FONCYT-PICT ( 2017-1818 , 2017-1820 ); ANID/FONDECYT Regular ( 1210195 , 1210176 , 1220995 ); ANID/FONDAP ( 15150012 ); ANID/PIA/ANILLOS ACT210096 ; ANID/FONDEF ID20I10152 , ID22I10029 ; and the Multi-Partner Consortium to Expand Dementia Research in Latin America (ReDLat), funded by the National Institutes of Aging of the National Institutes of Health under award number R01AG057234 , an Alzheimer's Association grant ( SG-20-725707-ReDLat ), the Rainwater Foundation , and the Global Brain Health Institute . SF is an Atlantic Fellow for Equity in Brain Health at the Global Brain Health Institute (GBHI) and is supported with funding from GBHI , BrainLat , ANID/FONDEF ID22I10029 , and CONICET . The content is solely the responsibility of the authors and does not represent the official views of these institutions.
Publisher Copyright:
© 2022
PY - 2022/12
Y1 - 2022/12
N2 - Brain functional networks have been traditionally studied considering only interactions between pairs of regions, neglecting the richer information encoded in higher orders of interactions. In consequence, most of the connectivity studies in neurodegeneration and dementia use standard pairwise metrics. Here, we developed a genuine high-order functional connectivity (HOFC) approach that captures interactions between 3 or more regions across spatiotemporal scales, delivering a more biologically plausible characterization of the pathophysiology of neurodegeneration. We applied HOFC to multimodal (electroencephalography [EEG], and functional magnetic resonance imaging [fMRI]) data from patients diagnosed with behavioral variant of frontotemporal dementia (bvFTD), Alzheimer's disease (AD), and healthy controls. HOFC revealed large effect sizes, which, in comparison to standard pairwise metrics, provided a more accurate and parsimonious characterization of neurodegeneration. The multimodal characterization of neurodegeneration revealed hypo and hyperconnectivity on medium to large-scale brain networks, with a larger contribution of the former. Regions as the amygdala, the insula, and frontal gyrus were associated with both effects, suggesting potential compensatory processes in hub regions. fMRI revealed hypoconnectivity in AD between regions of the default mode, salience, visual, and auditory networks, while in bvFTD between regions of the default mode, salience, and somatomotor networks. EEG revealed hypoconnectivity in the γ band between frontal, limbic, and sensory regions in AD, and in the δ band between frontal, temporal, parietal and posterior areas in bvFTD, suggesting additional pathophysiological processes that fMRI alone can not capture. Classification accuracy was comparable with standard biomarkers and robust against confounders such as sample size, age, education, and motor artifacts (from fMRI and EEG). We conclude that high-order interactions provide a detailed, EEG- and fMRI compatible, biologically plausible, and psychopathological-specific characterization of different neurodegenerative conditions.
AB - Brain functional networks have been traditionally studied considering only interactions between pairs of regions, neglecting the richer information encoded in higher orders of interactions. In consequence, most of the connectivity studies in neurodegeneration and dementia use standard pairwise metrics. Here, we developed a genuine high-order functional connectivity (HOFC) approach that captures interactions between 3 or more regions across spatiotemporal scales, delivering a more biologically plausible characterization of the pathophysiology of neurodegeneration. We applied HOFC to multimodal (electroencephalography [EEG], and functional magnetic resonance imaging [fMRI]) data from patients diagnosed with behavioral variant of frontotemporal dementia (bvFTD), Alzheimer's disease (AD), and healthy controls. HOFC revealed large effect sizes, which, in comparison to standard pairwise metrics, provided a more accurate and parsimonious characterization of neurodegeneration. The multimodal characterization of neurodegeneration revealed hypo and hyperconnectivity on medium to large-scale brain networks, with a larger contribution of the former. Regions as the amygdala, the insula, and frontal gyrus were associated with both effects, suggesting potential compensatory processes in hub regions. fMRI revealed hypoconnectivity in AD between regions of the default mode, salience, visual, and auditory networks, while in bvFTD between regions of the default mode, salience, and somatomotor networks. EEG revealed hypoconnectivity in the γ band between frontal, limbic, and sensory regions in AD, and in the δ band between frontal, temporal, parietal and posterior areas in bvFTD, suggesting additional pathophysiological processes that fMRI alone can not capture. Classification accuracy was comparable with standard biomarkers and robust against confounders such as sample size, age, education, and motor artifacts (from fMRI and EEG). We conclude that high-order interactions provide a detailed, EEG- and fMRI compatible, biologically plausible, and psychopathological-specific characterization of different neurodegenerative conditions.
KW - Biomarkers
KW - High-order interactions
KW - Machine learning
KW - Neural networks
KW - Neurodegeneration
KW - Neuroimaging
UR - http://www.scopus.com/inward/record.url?scp=85141782738&partnerID=8YFLogxK
U2 - 10.1016/j.nbd.2022.105918
DO - 10.1016/j.nbd.2022.105918
M3 - Article
AN - SCOPUS:85141782738
SN - 0969-9961
VL - 175
JO - Neurobiology of Disease
JF - Neurobiology of Disease
M1 - 105918
ER -