The impact of ROI extraction method for MEG connectivity estimation: Practical recommendations for the study of resting state data.
Magnetoencephalography and electroencephalography (M/EEG) seed-based connectivity analysis typically requires regions of interest (ROI)-based extraction of measures. M/EEG ROI-derived source activity can be treated in different ways. For instance, it is possible to average each ROI's time serie...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-12-01
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| Series: | NeuroImage |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S105381192300575X |
| _version_ | 1797405523217219584 |
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| author | Diandra Brkić Sara Sommariva Anna-Lisa Schuler Annalisa Pascarella Paolo Belardinelli Silvia L. Isabella Giovanni Di Pino Sara Zago Giulio Ferrazzi Javier Rasero Giorgio Arcara Daniele Marinazzo Giovanni Pellegrino |
| author_facet | Diandra Brkić Sara Sommariva Anna-Lisa Schuler Annalisa Pascarella Paolo Belardinelli Silvia L. Isabella Giovanni Di Pino Sara Zago Giulio Ferrazzi Javier Rasero Giorgio Arcara Daniele Marinazzo Giovanni Pellegrino |
| author_sort | Diandra Brkić |
| collection | DOAJ |
| description | Magnetoencephalography and electroencephalography (M/EEG) seed-based connectivity analysis typically requires regions of interest (ROI)-based extraction of measures. M/EEG ROI-derived source activity can be treated in different ways. For instance, it is possible to average each ROI's time series prior to calculating connectivity measures. Alternatively one can compute connectivity maps for each element of the ROI, prior to dimensionality reduction to obtain a single map. The impact of these different strategies on connectivity estimation is still unclear.Here, we address this question within a large MEG resting state cohort (N=113) and simulated data. We consider 68 ROIs (Desikan-Kiliany atlas), two measures of connectivity (phase locking value-PLV, and its imaginary counterpart- ciPLV), and three frequency bands (theta 4-8 Hz, alpha 9-12 Hz, beta 15-30 Hz). We consider four extraction methods: (i) mean, or (ii) PCA of the activity within the ROI before computing connectivity, (iii) average, or (iv) maximum connectivity after computing connectivity for each element of the seed. Connectivity outputs from these extraction strategies are then compared with hierarchical clustering, followed by direct contrasts across extraction methods. Finally, the results are validated by using a set of realistic simulations.We show that ROI-based connectivity maps vary remarkably across strategies in both connectivity magnitude and spatial distribution. Dimensionality reduction procedures conducted after computing connectivity are more similar to each-other, while PCA before approach is the most dissimilar to other approaches. Although differences across methods are consistent across frequency bands, they are influenced by the connectivity metric and ROI size. Greater differences were observed for ciPLV than PLV, and in larger ROIs. Realistic simulations confirmed that after aggregation procedures are generally more accurate but have lower specificity (higher rate of false positive connections). Although computationally demanding, after dimensionality reduction strategies should be preferred when higher sensitivity is desired. Given the remarkable differences across aggregation procedures, caution is warranted in comparing results across studies applying different extraction methods. |
| first_indexed | 2024-03-09T03:11:14Z |
| format | Article |
| id | doaj.art-abac9cc2b76a47b4a6fe87dbc6739f70 |
| institution | Directory Open Access Journal |
| issn | 1095-9572 |
| language | English |
| last_indexed | 2024-03-09T03:11:14Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj.art-abac9cc2b76a47b4a6fe87dbc6739f702023-12-04T05:21:23ZengElsevierNeuroImage1095-95722023-12-01284120424The impact of ROI extraction method for MEG connectivity estimation: Practical recommendations for the study of resting state data.Diandra Brkić0Sara Sommariva1Anna-Lisa Schuler2Annalisa Pascarella3Paolo Belardinelli4Silvia L. Isabella5Giovanni Di Pino6Sara Zago7Giulio Ferrazzi8Javier Rasero9Giorgio Arcara10Daniele Marinazzo11Giovanni Pellegrino12IRCCS San Camillo, Venice, ItalyDipartimento di Matematica, Università di Genova, Genova, ItalyMax Planck Institute for Human Cognitive and Brain Sciences, Leipzig, GermanyNational Research Council, Istituto per le Applicazioni del Calcolo “M. Picone”, Rome, ItalyCIMeC, University of Trento, Trento, Italy; Department of Neurology and Stroke, University of Tübingen, Germany; Hertie Institute for Clinical Brain Research, University of Tübingen, GermanyIRCCS San Camillo, Venice, Italy; Research Unit of Neurophysiology and Neuroengineering of Human-Technology Interaction (NeXTlab), Università Campus Bio-Medico di Roma, Rome, ItalyResearch Unit of Neurophysiology and Neuroengineering of Human-Technology Interaction (NeXTlab), Università Campus Bio-Medico di Roma, Rome, ItalyIRCCS San Camillo, Venice, ItalyPhilips Healthcare, Milan, ItalyCoAx Lab, Carnegie Mellon University, Pittsburgh, USA; School of Data Science, University of Virginia, Charlottesville, USA; Corresponding author at: CoAx Lab, Carnegie Mellon University, Pittsburgh, USA.IRCCS San Camillo, Venice, ItalyFaculty of Psychology and Educational Sciences, Department of Data Analysis, University of Ghent, Ghent, BelgiumDepartment of Clinical Neurological Sciences, Schulich School of Medicine & Dentistry, Western University, London, Ontario, CanadaMagnetoencephalography and electroencephalography (M/EEG) seed-based connectivity analysis typically requires regions of interest (ROI)-based extraction of measures. M/EEG ROI-derived source activity can be treated in different ways. For instance, it is possible to average each ROI's time series prior to calculating connectivity measures. Alternatively one can compute connectivity maps for each element of the ROI, prior to dimensionality reduction to obtain a single map. The impact of these different strategies on connectivity estimation is still unclear.Here, we address this question within a large MEG resting state cohort (N=113) and simulated data. We consider 68 ROIs (Desikan-Kiliany atlas), two measures of connectivity (phase locking value-PLV, and its imaginary counterpart- ciPLV), and three frequency bands (theta 4-8 Hz, alpha 9-12 Hz, beta 15-30 Hz). We consider four extraction methods: (i) mean, or (ii) PCA of the activity within the ROI before computing connectivity, (iii) average, or (iv) maximum connectivity after computing connectivity for each element of the seed. Connectivity outputs from these extraction strategies are then compared with hierarchical clustering, followed by direct contrasts across extraction methods. Finally, the results are validated by using a set of realistic simulations.We show that ROI-based connectivity maps vary remarkably across strategies in both connectivity magnitude and spatial distribution. Dimensionality reduction procedures conducted after computing connectivity are more similar to each-other, while PCA before approach is the most dissimilar to other approaches. Although differences across methods are consistent across frequency bands, they are influenced by the connectivity metric and ROI size. Greater differences were observed for ciPLV than PLV, and in larger ROIs. Realistic simulations confirmed that after aggregation procedures are generally more accurate but have lower specificity (higher rate of false positive connections). Although computationally demanding, after dimensionality reduction strategies should be preferred when higher sensitivity is desired. Given the remarkable differences across aggregation procedures, caution is warranted in comparing results across studies applying different extraction methods.http://www.sciencedirect.com/science/article/pii/S105381192300575XN/A |
| spellingShingle | Diandra Brkić Sara Sommariva Anna-Lisa Schuler Annalisa Pascarella Paolo Belardinelli Silvia L. Isabella Giovanni Di Pino Sara Zago Giulio Ferrazzi Javier Rasero Giorgio Arcara Daniele Marinazzo Giovanni Pellegrino The impact of ROI extraction method for MEG connectivity estimation: Practical recommendations for the study of resting state data. NeuroImage N/A |
| title | The impact of ROI extraction method for MEG connectivity estimation: Practical recommendations for the study of resting state data. |
| title_full | The impact of ROI extraction method for MEG connectivity estimation: Practical recommendations for the study of resting state data. |
| title_fullStr | The impact of ROI extraction method for MEG connectivity estimation: Practical recommendations for the study of resting state data. |
| title_full_unstemmed | The impact of ROI extraction method for MEG connectivity estimation: Practical recommendations for the study of resting state data. |
| title_short | The impact of ROI extraction method for MEG connectivity estimation: Practical recommendations for the study of resting state data. |
| title_sort | impact of roi extraction method for meg connectivity estimation practical recommendations for the study of resting state data |
| topic | N/A |
| url | http://www.sciencedirect.com/science/article/pii/S105381192300575X |
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