Dysfunction of attention switching networks in amyotrophic lateral sclerosis
Objective: To localise and characterise changes in cognitive networks in Amyotrophic Lateral Sclerosis (ALS) using source analysis of mismatch negativity (MMN) waveforms. Rationale: The MMN waveform has an increased average delay in ALS. MMN has been attributed to change detection and involuntary at...
| Main Authors: | , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2019-01-01
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| Series: | NeuroImage: Clinical |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2213158219300579 |
| _version_ | 1828422114085961728 |
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| author | Roisin McMackin Stefan Dukic Michael Broderick Parameswaran M. Iyer Marta Pinto-Grau Kieran Mohr Rangariroyashe Chipika Amina Coffey Teresa Buxo Christina Schuster Brighid Gavin Mark Heverin Peter Bede Niall Pender Edmund C. Lalor Muthuraman Muthuraman Orla Hardiman Bahman Nasseroleslami |
| author_facet | Roisin McMackin Stefan Dukic Michael Broderick Parameswaran M. Iyer Marta Pinto-Grau Kieran Mohr Rangariroyashe Chipika Amina Coffey Teresa Buxo Christina Schuster Brighid Gavin Mark Heverin Peter Bede Niall Pender Edmund C. Lalor Muthuraman Muthuraman Orla Hardiman Bahman Nasseroleslami |
| author_sort | Roisin McMackin |
| collection | DOAJ |
| description | Objective: To localise and characterise changes in cognitive networks in Amyotrophic Lateral Sclerosis (ALS) using source analysis of mismatch negativity (MMN) waveforms. Rationale: The MMN waveform has an increased average delay in ALS. MMN has been attributed to change detection and involuntary attention switching. This therefore indicates pathological impairment of the neural network components which generate these functions. Source localisation can mitigate the poor spatial resolution of sensor-level EEG analysis by associating the sensor-level signals to the contributing brain sources. The functional activity in each generating source can therefore be individually measured and investigated as a quantitative biomarker of impairment in ALS or its sub-phenotypes. Methods: MMN responses from 128-channel electroencephalography (EEG) recordings in 58 ALS patients and 39 healthy controls were localised to source by three separate localisation methods, including beamforming, dipole fitting and exact low resolution brain electromagnetic tomography. Results: Compared with controls, ALS patients showed significant increase in power of the left posterior parietal, central and dorsolateral prefrontal cortices (false discovery rate = 0.1). This change correlated with impaired cognitive flexibility (rho = 0.45, 0.45, 0.47, p = .042, .055, .031 respectively). ALS patients also exhibited a decrease in the power of dipoles representing activity in the inferior frontal (left: p = 5.16 × 10−6, right: p = 1.07 × 10−5) and left superior temporal gyri (p = 9.30 × 10−6). These patterns were detected across three source localisation methods. Decrease in right inferior frontal gyrus activity was a good discriminator of ALS patients from controls (AUROC = 0.77) and an excellent discriminator of C9ORF72 expansion-positive patients from controls (AUROC = 0.95). Interpretation: Source localization of evoked potentials can reliably discriminate patterns of functional network impairment in ALS and ALS subgroups during involuntary attention switching. The discriminative ability of the detected cognitive changes in specific brain regions are comparable to those of functional magnetic resonance imaging (fMRI).Source analysis of high-density EEG patterns has excellent potential to provide non-invasive, data-driven quantitative biomarkers of network disruption that could be harnessed as novel neurophysiology-based outcome measures in clinical trials. Keywords: Amyotrophic lateral sclerosis, Network, EEG, Cognition, Source localisation, Mismatch negativity |
| first_indexed | 2024-12-10T15:43:00Z |
| format | Article |
| id | doaj.art-51cf6a2513a040b0a4d81b3196b86c55 |
| institution | Directory Open Access Journal |
| issn | 2213-1582 |
| language | English |
| last_indexed | 2024-12-10T15:43:00Z |
| publishDate | 2019-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage: Clinical |
| spelling | doaj.art-51cf6a2513a040b0a4d81b3196b86c552022-12-22T01:43:03ZengElsevierNeuroImage: Clinical2213-15822019-01-0122Dysfunction of attention switching networks in amyotrophic lateral sclerosisRoisin McMackin0Stefan Dukic1Michael Broderick2Parameswaran M. Iyer3Marta Pinto-Grau4Kieran Mohr5Rangariroyashe Chipika6Amina Coffey7Teresa Buxo8Christina Schuster9Brighid Gavin10Mark Heverin11Peter Bede12Niall Pender13Edmund C. Lalor14Muthuraman Muthuraman15Orla Hardiman16Bahman Nasseroleslami17Academic Unit of Neurology, Trinity College Dublin, The University of Dublin, IrelandAcademic Unit of Neurology, Trinity College Dublin, The University of Dublin, IrelandAcademic Unit of Neurology, Trinity College Dublin, The University of Dublin, Ireland; Trinity Centre for Bioengineering, Trinity College Dublin, The University of Dublin, IrelandAcademic Unit of Neurology, Trinity College Dublin, The University of Dublin, Ireland; Beaumont Hospital Dublin, Department of Neurology, Dublin, IrelandAcademic Unit of Neurology, Trinity College Dublin, The University of Dublin, Ireland; Beaumont Hospital Dublin, Department of Psychology, Dublin, IrelandAcademic Unit of Neurology, Trinity College Dublin, The University of Dublin, IrelandAcademic Unit of Neurology, Trinity College Dublin, The University of Dublin, Ireland; Computational Neuroimaging Group, Trinity College Dublin, The University of Dublin, Ireland.Academic Unit of Neurology, Trinity College Dublin, The University of Dublin, Ireland; Beaumont Hospital Dublin, Department of Neurology, Dublin, IrelandAcademic Unit of Neurology, Trinity College Dublin, The University of Dublin, IrelandAcademic Unit of Neurology, Trinity College Dublin, The University of Dublin, Ireland; Computational Neuroimaging Group, Trinity College Dublin, The University of Dublin, Ireland.Academic Unit of Neurology, Trinity College Dublin, The University of Dublin, IrelandAcademic Unit of Neurology, Trinity College Dublin, The University of Dublin, IrelandAcademic Unit of Neurology, Trinity College Dublin, The University of Dublin, Ireland; Computational Neuroimaging Group, Trinity College Dublin, The University of Dublin, Ireland.Academic Unit of Neurology, Trinity College Dublin, The University of Dublin, Ireland; Beaumont Hospital Dublin, Department of Neurology, Dublin, IrelandAcademic Unit of Neurology, Trinity College Dublin, The University of Dublin, Ireland; Trinity College Institute of Neuroscience, Trinity College Dublin, The University of Dublin, Ireland.; Department of Biomedical Engineering, University of Rochester, Rochester, New York, USA.Movement Disorders and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing Unit, Department of Neurology, Johannes-Gutenberg-University Hospital, Mainz, GermanyAcademic Unit of Neurology, Trinity College Dublin, The University of Dublin, Ireland; Beaumont Hospital Dublin, Department of Neurology, Dublin, Ireland; Computational Neuroimaging Group, Trinity College Dublin, The University of Dublin, Ireland.; Corresponding author at: Academic Unit of Neurology, Trinity College Dublin, The University of Dublin, Room 5.43, Trinity Biomedical Sciences Institute, 152-160 Pearse Street, Dublin D02 R590, Ireland.Academic Unit of Neurology, Trinity College Dublin, The University of Dublin, IrelandObjective: To localise and characterise changes in cognitive networks in Amyotrophic Lateral Sclerosis (ALS) using source analysis of mismatch negativity (MMN) waveforms. Rationale: The MMN waveform has an increased average delay in ALS. MMN has been attributed to change detection and involuntary attention switching. This therefore indicates pathological impairment of the neural network components which generate these functions. Source localisation can mitigate the poor spatial resolution of sensor-level EEG analysis by associating the sensor-level signals to the contributing brain sources. The functional activity in each generating source can therefore be individually measured and investigated as a quantitative biomarker of impairment in ALS or its sub-phenotypes. Methods: MMN responses from 128-channel electroencephalography (EEG) recordings in 58 ALS patients and 39 healthy controls were localised to source by three separate localisation methods, including beamforming, dipole fitting and exact low resolution brain electromagnetic tomography. Results: Compared with controls, ALS patients showed significant increase in power of the left posterior parietal, central and dorsolateral prefrontal cortices (false discovery rate = 0.1). This change correlated with impaired cognitive flexibility (rho = 0.45, 0.45, 0.47, p = .042, .055, .031 respectively). ALS patients also exhibited a decrease in the power of dipoles representing activity in the inferior frontal (left: p = 5.16 × 10−6, right: p = 1.07 × 10−5) and left superior temporal gyri (p = 9.30 × 10−6). These patterns were detected across three source localisation methods. Decrease in right inferior frontal gyrus activity was a good discriminator of ALS patients from controls (AUROC = 0.77) and an excellent discriminator of C9ORF72 expansion-positive patients from controls (AUROC = 0.95). Interpretation: Source localization of evoked potentials can reliably discriminate patterns of functional network impairment in ALS and ALS subgroups during involuntary attention switching. The discriminative ability of the detected cognitive changes in specific brain regions are comparable to those of functional magnetic resonance imaging (fMRI).Source analysis of high-density EEG patterns has excellent potential to provide non-invasive, data-driven quantitative biomarkers of network disruption that could be harnessed as novel neurophysiology-based outcome measures in clinical trials. Keywords: Amyotrophic lateral sclerosis, Network, EEG, Cognition, Source localisation, Mismatch negativityhttp://www.sciencedirect.com/science/article/pii/S2213158219300579 |
| spellingShingle | Roisin McMackin Stefan Dukic Michael Broderick Parameswaran M. Iyer Marta Pinto-Grau Kieran Mohr Rangariroyashe Chipika Amina Coffey Teresa Buxo Christina Schuster Brighid Gavin Mark Heverin Peter Bede Niall Pender Edmund C. Lalor Muthuraman Muthuraman Orla Hardiman Bahman Nasseroleslami Dysfunction of attention switching networks in amyotrophic lateral sclerosis NeuroImage: Clinical |
| title | Dysfunction of attention switching networks in amyotrophic lateral sclerosis |
| title_full | Dysfunction of attention switching networks in amyotrophic lateral sclerosis |
| title_fullStr | Dysfunction of attention switching networks in amyotrophic lateral sclerosis |
| title_full_unstemmed | Dysfunction of attention switching networks in amyotrophic lateral sclerosis |
| title_short | Dysfunction of attention switching networks in amyotrophic lateral sclerosis |
| title_sort | dysfunction of attention switching networks in amyotrophic lateral sclerosis |
| url | http://www.sciencedirect.com/science/article/pii/S2213158219300579 |
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