Genuine high-order interactions in brain networks and neurodegeneration
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 metri...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-12-01
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| Series: | Neurobiology of Disease |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0969996122003102 |
| _version_ | 1798006203495743488 |
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| author | Rubén Herzog Fernando E. Rosas Robert Whelan Sol Fittipaldi Hernando Santamaria-Garcia Josephine Cruzat Agustina Birba Sebastian Moguilner Enzo Tagliazucchi Pavel Prado Agustin Ibanez |
| author_facet | Rubén Herzog Fernando E. Rosas Robert Whelan Sol Fittipaldi Hernando Santamaria-Garcia Josephine Cruzat Agustina Birba Sebastian Moguilner Enzo Tagliazucchi Pavel Prado Agustin Ibanez |
| author_sort | Rubén Herzog |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T12:51:13Z |
| format | Article |
| id | doaj.art-ebffd12c0d6440dda513a78cce3c01ce |
| institution | Directory Open Access Journal |
| issn | 1095-953X |
| language | English |
| last_indexed | 2024-04-11T12:51:13Z |
| publishDate | 2022-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Neurobiology of Disease |
| spelling | doaj.art-ebffd12c0d6440dda513a78cce3c01ce2022-12-22T04:23:12ZengElsevierNeurobiology of Disease1095-953X2022-12-01175105918Genuine high-order interactions in brain networks and neurodegenerationRubén Herzog0Fernando E. Rosas1Robert Whelan2Sol Fittipaldi3Hernando Santamaria-Garcia4Josephine Cruzat5Agustina Birba6Sebastian Moguilner7Enzo Tagliazucchi8Pavel Prado9Agustin Ibanez10Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Fundación para el Estudio de la Conciencia Humana (EcoH), ChileFundación para el Estudio de la Conciencia Humana (EcoH), Chile; Centre for Psychedelic Research, Department of Brain Sciences, Imperial College London, UK; Data Science Institute, Imperial College London, UK; Centre for Complexity Science, Imperial College London, UK; Department of Informatics, University of Sussex, Brighton, UKGlobal Brain Health Institute (GBHI), Trinity College Dublin, Dublin 2, IrelandLatin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Global Brain Health Institute (GBHI), Trinity College Dublin, Dublin 2, Ireland; Cognitive Neuroscience Center (CNC), Universidad de San Andrés & CONICET, Buenos Aires, ArgentinaLatin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, ChileLatin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Fundación para el Estudio de la Conciencia Humana (EcoH), ChileCognitive Neuroscience Center (CNC), Universidad de San Andrés & CONICET, Buenos Aires, ArgentinaLatin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, ChileLatin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Buenos Aires Physics Institute and Physics Department, University of Buenos Aires, Buenos Aires, ArgentinaLatin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Corresponding authors at: Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile.Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Global Brain Health Institute (GBHI), Trinity College Dublin, Dublin 2, Ireland; Cognitive Neuroscience Center (CNC), Universidad de San Andrés & CONICET, Buenos Aires, Argentina; Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), CA, USA; Corresponding authors at: Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile.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.http://www.sciencedirect.com/science/article/pii/S0969996122003102NeurodegenerationNeuroimagingNeural networksHigh-order interactionsMachine learningBiomarkers |
| spellingShingle | Rubén Herzog Fernando E. Rosas Robert Whelan Sol Fittipaldi Hernando Santamaria-Garcia Josephine Cruzat Agustina Birba Sebastian Moguilner Enzo Tagliazucchi Pavel Prado Agustin Ibanez Genuine high-order interactions in brain networks and neurodegeneration Neurobiology of Disease Neurodegeneration Neuroimaging Neural networks High-order interactions Machine learning Biomarkers |
| title | Genuine high-order interactions in brain networks and neurodegeneration |
| title_full | Genuine high-order interactions in brain networks and neurodegeneration |
| title_fullStr | Genuine high-order interactions in brain networks and neurodegeneration |
| title_full_unstemmed | Genuine high-order interactions in brain networks and neurodegeneration |
| title_short | Genuine high-order interactions in brain networks and neurodegeneration |
| title_sort | genuine high order interactions in brain networks and neurodegeneration |
| topic | Neurodegeneration Neuroimaging Neural networks High-order interactions Machine learning Biomarkers |
| url | http://www.sciencedirect.com/science/article/pii/S0969996122003102 |
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