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Mao X, Zhang Z, Yang Y, Chen Y, Wang Y, Wang W. Characteristics of different Mandarin pronunciation element perception: evidence based on a multifeature paradigm for recording MMN and P3a components of phonemic changes in speech sounds. Front Neurosci 2024; 17:1277129. [PMID: 38264493 PMCID: PMC10804857 DOI: 10.3389/fnins.2023.1277129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 12/18/2023] [Indexed: 01/25/2024] Open
Abstract
Background As a tonal language, Mandarin Chinese has the following pronunciation elements for each syllable: the vowel, consonant, tone, duration, and intensity. Revealing the characteristics of auditory-related cortical processing of these different pronunciation elements is interesting. Methods A Mandarin pronunciation multifeature paradigm was designed, during which a standard stimulus and five different phonemic deviant stimuli were presented. The electroencephalogram (EEG) data were recorded with 256-electrode high-density EEG equipment. Time-domain and source localization analyses were conducted to demonstrate waveform characteristics and locate the sources of the cortical processing of mismatch negativity (MMN) and P3a components following different stimuli. Results Vowel and consonant differences elicited distinct MMN and P3a components, but tone and duration differences did not. Intensity differences elicited distinct MMN components but not P3a components. For MMN and P3a components, the activated cortical areas were mainly in the frontal-temporal lobe. However, the regions and intensities of the cortical activation were significantly different among the components for the various deviant stimuli. The activated cortical areas of the MMN and P3a components elicited by vowels and consonants seemed to be larger and show more intense activation. Conclusion The auditory processing centers use different auditory-related cognitive resources when processing different Mandarin pronunciation elements. Vowels and consonants carry more information for speech comprehension; moreover, more neurons in the cortex may be involved in the recognition and cognitive processing of these elements.
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Affiliation(s)
- Xiang Mao
- Department of Otorhinolaryngology Head and Neck Surgery, Tianjin First Central Hospital, Tianjin, China
- Institute of Otolaryngology of Tianjin, Tianjin, China
- Key Laboratory of Auditory Speech and Balance Medicine, Tianjin, China
- Key Medical Discipline of Tianjin (Otolaryngology), Tianjin, China
- Otolaryngology Clinical Quality Control Centre, Tianjin, China
| | - Ziyue Zhang
- Department of Otorhinolaryngology Head and Neck Surgery, Tianjin First Central Hospital, Tianjin, China
- Institute of Otolaryngology of Tianjin, Tianjin, China
- Key Laboratory of Auditory Speech and Balance Medicine, Tianjin, China
- Key Medical Discipline of Tianjin (Otolaryngology), Tianjin, China
- Otolaryngology Clinical Quality Control Centre, Tianjin, China
| | - Yijing Yang
- Department of Otorhinolaryngology Head and Neck Surgery, Tianjin First Central Hospital, Tianjin, China
- Institute of Otolaryngology of Tianjin, Tianjin, China
- Key Laboratory of Auditory Speech and Balance Medicine, Tianjin, China
- Key Medical Discipline of Tianjin (Otolaryngology), Tianjin, China
- Otolaryngology Clinical Quality Control Centre, Tianjin, China
| | - Yu Chen
- Department of Otorhinolaryngology Head and Neck Surgery, Tianjin First Central Hospital, Tianjin, China
- Institute of Otolaryngology of Tianjin, Tianjin, China
- Key Laboratory of Auditory Speech and Balance Medicine, Tianjin, China
- Key Medical Discipline of Tianjin (Otolaryngology), Tianjin, China
- Otolaryngology Clinical Quality Control Centre, Tianjin, China
| | - Yue Wang
- Department of Otorhinolaryngology Head and Neck Surgery, Tianjin First Central Hospital, Tianjin, China
- Institute of Otolaryngology of Tianjin, Tianjin, China
- Key Laboratory of Auditory Speech and Balance Medicine, Tianjin, China
- Key Medical Discipline of Tianjin (Otolaryngology), Tianjin, China
- Otolaryngology Clinical Quality Control Centre, Tianjin, China
| | - Wei Wang
- Department of Otorhinolaryngology Head and Neck Surgery, Tianjin First Central Hospital, Tianjin, China
- Institute of Otolaryngology of Tianjin, Tianjin, China
- Key Laboratory of Auditory Speech and Balance Medicine, Tianjin, China
- Key Medical Discipline of Tianjin (Otolaryngology), Tianjin, China
- Otolaryngology Clinical Quality Control Centre, Tianjin, China
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2
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Jellinger KA. The Spectrum of Cognitive Dysfunction in Amyotrophic Lateral Sclerosis: An Update. Int J Mol Sci 2023; 24:14647. [PMID: 37834094 PMCID: PMC10572320 DOI: 10.3390/ijms241914647] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 09/21/2023] [Accepted: 09/21/2023] [Indexed: 10/15/2023] Open
Abstract
Cognitive dysfunction is an important non-motor symptom in amyotrophic lateral sclerosis (ALS) that has a negative impact on survival and caregiver burden. It shows a wide spectrum ranging from subjective cognitive decline to frontotemporal dementia (FTD) and covers various cognitive domains, mainly executive/attention, language and verbal memory deficits. The frequency of cognitive impairment across the different ALS phenotypes ranges from 30% to 75%, with up to 45% fulfilling the criteria of FTD. Significant genetic, clinical, and pathological heterogeneity reflects deficits in various cognitive domains. Modern neuroimaging studies revealed frontotemporal degeneration and widespread involvement of limbic and white matter systems, with hypometabolism of the relevant areas. Morphological substrates are frontotemporal and hippocampal atrophy with synaptic loss, associated with TDP-43 and other co-pathologies, including tau deposition. Widespread functional disruptions of motor and extramotor networks, as well as of frontoparietal, frontostriatal and other connectivities, are markers for cognitive deficits in ALS. Cognitive reserve may moderate the effect of brain damage but is not protective against cognitive decline. The natural history of cognitive dysfunction in ALS and its relationship to FTD are not fully understood, although there is an overlap between the ALS variants and ALS-related frontotemporal syndromes, suggesting a differential vulnerability of motor and non-motor networks. An assessment of risks or the early detection of brain connectivity signatures before structural changes may be helpful in investigating the pathophysiological mechanisms of cognitive impairment in ALS, which might even serve as novel targets for effective disease-modifying therapies.
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Affiliation(s)
- Kurt A Jellinger
- Institute of Clinical Neurobiology, Alberichgasse 5/13, A-1150 Vienna, Austria
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3
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Functional alterations in large-scale resting-state networks of amyotrophic lateral sclerosis: A multi-site study across Canada and the United States. PLoS One 2022; 17:e0269154. [PMID: 35709100 PMCID: PMC9202847 DOI: 10.1371/journal.pone.0269154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Accepted: 05/16/2022] [Indexed: 11/19/2022] Open
Abstract
Amyotrophic lateral sclerosis (ALS) is a multisystem neurodegenerative disorder characterized by progressive degeneration of upper motor neurons and lower motor neurons, and frontotemporal regions resulting in impaired bulbar, limb, and cognitive function. Magnetic resonance imaging studies have reported cortical and subcortical brain involvement in the pathophysiology of ALS. The present study investigates the functional integrity of resting-state networks (RSNs) and their importance in ALS. Intra- and inter-network resting-state functional connectivity (Rs-FC) was examined using an independent component analysis approach in a large multi-center cohort. A total of 235 subjects (120 ALS patients; 115 healthy controls (HC) were recruited across North America through the Canadian ALS Neuroimaging Consortium (CALSNIC). Intra-network and inter-network Rs-FC was evaluated by the FSL-MELODIC and FSLNets software packages. As compared to HC, ALS patients displayed higher intra-network Rs-FC in the sensorimotor, default mode, right and left fronto-parietal, and orbitofrontal RSNs, and in previously undescribed networks including auditory, dorsal attention, basal ganglia, medial temporal, ventral streams, and cerebellum which negatively correlated with disease severity. Furthermore, ALS patients displayed higher inter-network Rs-FC between the orbitofrontal and basal ganglia RSNs which negatively correlated with cognitive impairment. In summary, in ALS there is an increase in intra- and inter-network functional connectivity of RSNs underpinning both motor and cognitive impairment. Moreover, the large multi-center CALSNIC dataset permitted the exploration of RSNs in unprecedented detail, revealing previously undescribed network involvement in ALS.
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4
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Dukic S, McMackin R, Costello E, Metzger M, Buxo T, Fasano A, Chipika R, Pinto-Grau M, Schuster C, Hammond M, Heverin M, Coffey A, Broderick M, Iyer PM, Mohr K, Gavin B, McLaughlin R, Pender N, Bede P, Muthuraman M, van den Berg L, Hardiman O, Nasseroleslami B. Resting-state EEG reveals four subphenotypes of amyotrophic lateral sclerosis. Brain 2021; 145:621-631. [PMID: 34791079 PMCID: PMC9014749 DOI: 10.1093/brain/awab322] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 06/25/2021] [Accepted: 07/26/2021] [Indexed: 11/14/2022] Open
Abstract
Amyotrophic lateral sclerosis is a devastating disease characterized primarily by motor system degeneration, with clinical evidence of cognitive and behavioural change in up to 50% of cases. Amyotrophic lateral sclerosis is both clinically and biologically heterogeneous. Subgrouping is currently undertaken using clinical parameters, such as site of symptom onset (bulbar or spinal), burden of disease (based on the modified El Escorial Research Criteria) and genomics in those with familial disease. However, with the exception of genomics, these subcategories do not take into account underlying disease pathobiology, and are not fully predictive of disease course or prognosis. Recently, we have shown that resting-state EEG can reliably and quantitatively capture abnormal patterns of motor and cognitive network disruption in amyotrophic lateral sclerosis. These network disruptions have been identified across multiple frequency bands, and using measures of neural activity (spectral power) and connectivity (comodulation of activity by amplitude envelope correlation and synchrony by imaginary coherence) on source-localized brain oscillations from high-density EEG. Using data-driven methods (similarity network fusion and spectral clustering), we have now undertaken a clustering analysis to identify disease subphenotypes and to determine whether different patterns of disruption are predictive of disease outcome. We show that amyotrophic lateral sclerosis patients (n = 95) can be subgrouped into four phenotypes with distinct neurophysiological profiles. These clusters are characterized by varying degrees of disruption in the somatomotor (α-band synchrony), frontotemporal (β-band neural activity and γl-band synchrony) and frontoparietal (γl-band comodulation) networks, which reliably correlate with distinct clinical profiles and different disease trajectories. Using an in-depth stability analysis, we show that these clusters are statistically reproducible and robust, remain stable after reassessment using a follow-up EEG session, and continue to predict the clinical trajectory and disease outcome. Our data demonstrate that novel phenotyping using neuroelectric signal analysis can distinguish disease subtypes based exclusively on different patterns of network disturbances. These patterns may reflect underlying disease neurobiology. The identification of amyotrophic lateral sclerosis subtypes based on profiles of differential impairment in neuronal networks has clear potential in future stratification for clinical trials. Advanced network profiling in amyotrophic lateral sclerosis can also underpin new therapeutic strategies that are based on principles of neurobiology and designed to modulate network disruption.
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Affiliation(s)
- Stefan Dukic
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Ireland.,Department of Neurology, University Medical Centre Utrecht Brain Centre, Utrecht University, Utrecht, The Netherlands
| | - Roisin McMackin
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Ireland
| | - Emmet Costello
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Ireland
| | - Marjorie Metzger
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Ireland
| | - Teresa Buxo
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Ireland
| | - Antonio Fasano
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Ireland
| | - Rangariroyashe Chipika
- Computational Neuroimaging Group, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Ireland
| | - Marta Pinto-Grau
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Ireland
| | - Christina Schuster
- Computational Neuroimaging Group, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Ireland
| | - Michaela Hammond
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Ireland
| | - Mark Heverin
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Ireland
| | - Amina Coffey
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Ireland
| | - Michael Broderick
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Ireland
| | - Parameswaran M Iyer
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Ireland
| | - Kieran Mohr
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Ireland
| | - Brighid Gavin
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Ireland
| | - Russell McLaughlin
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Ireland
| | - Niall Pender
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Ireland
| | - Peter Bede
- Computational Neuroimaging Group, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Ireland
| | - Muthuraman Muthuraman
- Movement disorders and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing Unit, Department of Neurology, Johannes-Gutenberg-University Hospital, Mainz, Germany
| | - Leonard van den Berg
- Department of Neurology, University Medical Centre Utrecht Brain Centre, Utrecht University, Utrecht, The Netherlands
| | - Orla Hardiman
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Ireland.,Trinity College Institute of Neuroscience, Trinity College Dublin, University of Dublin, Ireland.,Department of Neurology, Beaumont Hospital, Dublin, Ireland
| | - Bahman Nasseroleslami
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Ireland
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5
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McKenna MC, Corcia P, Couratier P, Siah WF, Pradat PF, Bede P. Frontotemporal Pathology in Motor Neuron Disease Phenotypes: Insights From Neuroimaging. Front Neurol 2021; 12:723450. [PMID: 34484106 PMCID: PMC8415268 DOI: 10.3389/fneur.2021.723450] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 07/22/2021] [Indexed: 01/18/2023] Open
Abstract
Frontotemporal involvement has been extensively investigated in amyotrophic lateral sclerosis (ALS) but remains relatively poorly characterized in other motor neuron disease (MND) phenotypes such as primary lateral sclerosis (PLS), progressive muscular atrophy (PMA), spinal muscular atrophy (SMA), spinal bulbar muscular atrophy (SBMA), post poliomyelitis syndrome (PPS), and hereditary spastic paraplegia (HSP). This review focuses on insights from structural, metabolic, and functional neuroimaging studies that have advanced our understanding of extra-motor disease burden in these phenotypes. The imaging literature is limited in the majority of these conditions and frontotemporal involvement has been primarily evaluated by neuropsychology and post mortem studies. Existing imaging studies reveal that frontotemporal degeneration can be readily detected in ALS and PLS, varying degree of frontotemporal pathology may be captured in PMA, SBMA, and HSP, SMA exhibits cerebral involvement without regional predilection, and there is limited evidence for cerebral changes in PPS. Our review confirms the heterogeneity extra-motor pathology across the spectrum of MNDs and highlights the role of neuroimaging in characterizing anatomical patterns of disease burden in vivo. Despite the contribution of neuroimaging to MND research, sample size limitations, inclusion bias, attrition rates in longitudinal studies, and methodological constraints need to be carefully considered. Frontotemporal involvement is a quintessential clinical facet of MND which has important implications for screening practices, individualized management strategies, participation in clinical trials, caregiver burden, and resource allocation. The academic relevance of imaging frontotemporal pathology in MND spans from the identification of genetic variants, through the ascertainment of presymptomatic changes to the design of future epidemiology studies.
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Affiliation(s)
- Mary Clare McKenna
- Computational Neuroimaging Group, Trinity College Dublin, Dublin, Ireland
| | - Philippe Corcia
- Department of Neurology-Neurophysiology, CRMR ALS, Tours, France.,UMR 1253 iBrain, University of Tours, Tours, France.,LITORALS, Federation of ALS Centres: Tours-Limoges, Limoges, France
| | - Philippe Couratier
- LITORALS, Federation of ALS Centres: Tours-Limoges, Limoges, France.,ALS Centre, Limoges University Hospital (CHU de Limoges), Limoges, France
| | - We Fong Siah
- Computational Neuroimaging Group, Trinity College Dublin, Dublin, Ireland
| | | | - Peter Bede
- Computational Neuroimaging Group, Trinity College Dublin, Dublin, Ireland.,Pitié-Salpêtrière University Hospital, Sorbonne University, Paris, France
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6
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Abstract
PURPOSE OF REVIEW This review draws together the most recent findings in ALS biomarker research from biochemical, imaging and neurophysiology techniques. RECENT FINDINGS The potential of circulating RNA is highlighted, including new retrieval techniques. With ongoing genetic clinical trials, the need for pharmacodynamic biomarkers is essential. There is a strong case for neurofilament proteins being validated in ALS; their biomarker profile is discussed. Oxidative stress and neuroinflammation studies offer insight into disease mechanisms and offer good biomarker potential. Recent metabolic studies include investigation of lipid profiles, creatinine and ferritin. The potential of chitinase proteins as pharmacodynamic and prognostic biomarkers is highlighted. The role of tau and amyloidβ is debated, as evidenced by the articles presented here. Proteomic approaches provide unbiased discoveries of novel biomarkers, together with confirmation of previous findings. The use of imaging techniques is outlined to demonstrate selective atrophy, volume loss, muscle and tract involvement. In-vivo imaging is discussed with reference to histone deacetylase, oxidative stress, neuroinflammation and metabolic changes. New applications of electrophysiology demonstrate objective muscle biomarkers and brain network perturbations. SUMMARY The biomarker research field continues to provide insight into the disease. Multicentre collaborations are needed to validate these promising recent findings.
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7
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Cognitive dysfunction in amyotrophic lateral sclerosis: can we predict it? Neurol Sci 2021; 42:2211-2222. [PMID: 33772353 PMCID: PMC8159827 DOI: 10.1007/s10072-021-05188-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 03/15/2021] [Indexed: 01/26/2023]
Abstract
Background and aim Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder characterized by the degeneration of both upper and lower motoneurons in the brain and spinal cord leading to motor and extra-motor symptoms. Although traditionally considered a pure motor disease, recent evidences suggest that ALS is a multisystem disorder. Neuropsychological alterations, in fact, are observed in more than 50% of patients: while executive dysfunctions have been firstly identified, alterations in verbal fluency, behavior, and pragmatic and social cognition have also been described. Detecting and monitoring ALS cognitive and behavioral impairment even at early disease stages is likely to have staging and prognostic implications, and it may impact the enrollment in future clinical trials. During the last 10 years, humoral, radiological, neurophysiological, and genetic biomarkers have been reported in ALS, and some of them seem to potentially correlate to cognitive and behavioral impairment of patients. In this review, we sought to give an up-to-date state of the art of neuropsychological alterations in ALS: we will describe tests used to detect cognitive and behavioral impairment, and we will focus on promising non-invasive biomarkers to detect pre-clinical cognitive decline. Conclusions To date, the research on humoral, radiological, neurophysiological, and genetic correlates of neuropsychological alterations is at the early stage, and no conclusive longitudinal data have been published. Further and longitudinal studies on easily accessible and quantifiable biomarkers are needed to clarify the time course and the evolution of cognitive and behavioral impairments of ALS patients.
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8
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McMackin R, Dukic S, Costello E, Pinto-Grau M, McManus L, Broderick M, Chipika R, Iyer PM, Heverin M, Bede P, Muthuraman M, Pender N, Hardiman O, Nasseroleslami B. Cognitive network hyperactivation and motor cortex decline correlate with ALS prognosis. Neurobiol Aging 2021; 104:57-70. [PMID: 33964609 DOI: 10.1016/j.neurobiolaging.2021.03.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Revised: 02/26/2021] [Accepted: 03/02/2021] [Indexed: 02/07/2023]
Abstract
We aimed to quantitatively characterize progressive brain network disruption in Amyotrophic Lateral Sclerosis (ALS) during cognition using the mismatch negativity (MMN), an electrophysiological index of attention switching. We measured the MMN using 128-channel EEG longitudinally (2-5 timepoints) in 60 ALS patients and cross-sectionally in 62 healthy controls. Using dipole fitting and linearly constrained minimum variance beamforming we investigated cortical source activity changes over time. In ALS, the inferior frontal gyri (IFG) show significantly lower baseline activity compared to controls. The right IFG and both superior temporal gyri (STG) become progressively hyperactive longitudinally. By contrast, the left motor and dorsolateral prefrontal cortices are initially hyperactive, declining progressively. Baseline motor hyperactivity correlates with cognitive disinhibition, and lower baseline IFG activities correlate with motor decline rate, while left dorsolateral prefrontal activity predicted cognitive and behavioural impairment. Shorter survival correlates with reduced baseline IFG and STG activity and later STG hyperactivation. Source-resolved EEG facilitates quantitative characterization of symptom-associated and symptom-preceding motor and cognitive-behavioral cortical network decline in ALS.
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Affiliation(s)
- Roisin McMackin
- Academic Unit of Neurology, Trinity College Dublin, the University of Dublin, Dublin 2, Ireland
| | - Stefan Dukic
- Academic Unit of Neurology, Trinity College Dublin, the University of Dublin, Dublin 2, Ireland
| | - Emmet Costello
- Academic Unit of Neurology, Trinity College Dublin, the University of Dublin, Dublin 2, Ireland
| | - Marta Pinto-Grau
- Academic Unit of Neurology, Trinity College Dublin, the University of Dublin, Dublin 2, Ireland; Department of Neurology, University Medical Centre Utrecht Brain Centre, Utrecht University, Utrecht, The Netherlands
| | - Lara McManus
- Academic Unit of Neurology, Trinity College Dublin, the University of Dublin, Dublin 2, Ireland
| | - Michael Broderick
- Academic Unit of Neurology, Trinity College Dublin, the University of Dublin, Dublin 2, Ireland; Trinity Centre for Bioengineering, Trinity College Dublin, the University of Dublin, Dublin 2, Ireland
| | - Rangariroyashe Chipika
- Academic Unit of Neurology, Trinity College Dublin, the University of Dublin, Dublin 2, Ireland; Computational Neuroimaging Group, Trinity College Dublin, the University of Dublin, Dublin 2, Ireland
| | - Parameswaran M Iyer
- Academic Unit of Neurology, Trinity College Dublin, the University of Dublin, Dublin 2, Ireland; Beaumont Hospital Dublin, Department of Neurology, Dublin 9, Ireland
| | - Mark Heverin
- Academic Unit of Neurology, Trinity College Dublin, the University of Dublin, Dublin 2, Ireland
| | - Peter Bede
- Academic Unit of Neurology, Trinity College Dublin, the University of Dublin, Dublin 2, Ireland; Computational Neuroimaging Group, Trinity College Dublin, the University of Dublin, Dublin 2, Ireland
| | - Muthuraman Muthuraman
- Biomedical Statistics and Multimodal Signal Processing Unit, Department of Neurology, Johannes-Gutenberg-University Hospital, Mainz, Germany
| | - Niall Pender
- Academic Unit of Neurology, Trinity College Dublin, the University of Dublin, Dublin 2, Ireland; Department of Neurology, University Medical Centre Utrecht Brain Centre, Utrecht University, Utrecht, The Netherlands; Beaumont Hospital Dublin, Department of Neurology, Dublin 9, Ireland
| | - Orla Hardiman
- Academic Unit of Neurology, Trinity College Dublin, the University of Dublin, Dublin 2, Ireland; Beaumont Hospital Dublin, Department of Neurology, Dublin 9, Ireland.
| | - Bahman Nasseroleslami
- Academic Unit of Neurology, Trinity College Dublin, the University of Dublin, Dublin 2, Ireland
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9
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Impaired pain processing and its association with attention disturbance in patients with amyotrophic lateral sclerosis. Neurol Sci 2021; 42:3327-3335. [PMID: 33398509 DOI: 10.1007/s10072-020-05028-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Accepted: 12/23/2020] [Indexed: 10/22/2022]
Abstract
BACKGROUND Cognitive dysfunction characterized by executive dysfunction and persistent attention function has been reported in patients with amyotrophic lateral sclerosis (ALS); however, it is unclear if this contributes to the pain processing deficits associated with the disease. OBJECTIVE We clarified the relationship between pain processing and both cognitive function and sensory symptoms in patients with ALS. METHODS We enrolled 23 patients with ALS and 14 healthy control subjects. We examined pain-related somatosensory evoked potentials (SEPs) using an intra-epidermal needle electrode. We evaluated cognitive function and the clinical characteristics of sensation and analyzed their relationships with pain-related SEPs. RESULTS Pain-related SEP amplitudes were significantly lower, while the rate of amplitude attenuation due to habituation or change in attention was significantly greater in patients with ALS than in control subjects. There were no significant differences in pain-related SEP parameters between patients with or without sensory symptoms. Instead, pain-related SEP amplitude and its rate of attenuation were correlated with cognitive dysfunction, particularly with attention domains. CONCLUSIONS Our results suggest that attention deficit, but not sensory nerve involvement, is a major cause of the alterations in pain-related SEP in patients with ALS.
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10
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Ma X, Lu F, Chen H, Hu C, Wang J, Zhang S, Zhang S, Yang G, Zhang J. Static and dynamic alterations in the amplitude of low-frequency fluctuation in patients with amyotrophic lateral sclerosis. PeerJ 2020; 8:e10052. [PMID: 33194375 PMCID: PMC7643554 DOI: 10.7717/peerj.10052] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 09/07/2020] [Indexed: 01/10/2023] Open
Abstract
Background Static changes in local brain activity in patients suffering from amyotrophic lateral sclerosis (ALS) have been studied. However, the dynamic characteristics of local brain activity are poorly understood. Whether dynamic alterations could differentiate patients with ALS from healthy controls (HCs) remains unclear. Methods A total of 54 patients with ALS (mean age = 48.71 years, male/female = 36/18) and 54 (mean age = 48.30 years, male/female = 36/18) HCs underwent magnetic resonance imaging scans. To depict static alterations in cortical activity, amplitude of low-frequency fluctuations (ALFF) which measures the total power of regional activity was computed. Dynamic ALFF (d-ALFF) from all subjects was calculated using a sliding-window approach. Statistical differences in ALFF and d-ALFF between both groups were used as features to explore whether they could differentiate ALS from HC through support vector machine method. Results In contrast with HCs, patients with ALS displayed increased ALFF in the right inferior temporal gyrus and bilateral frontal gyrus and decreased ALFF in the left middle occipital gyrus and left precentral gyrus. Furthermore, patients with ALS demonstrated lower d-ALFF in widespread regions, including the right lingual gyrus, left superior temporal gyrus, bilateral precentral gyrus, and left paracentral lobule by comparison with HCs. In addition, the ALFF in the left superior orbitofrontal gyrus had a tendency of correlation with ALSFRS-R score and disease progression rate. The classification performance in distinguishing ALS was higher with both features of ALFF and d-ALFF than that with a single approach. Conclusions Decreased dynamic brain activity in the precentral gyrus, paracentral gyrus, lingual gyrus, and temporal regions was found in the ALS group. The combined ALFF and d-ALFF could distinguish ALS from HCs with a higher accuracy than ALFF and d-ALFF alone. These findings may provide important evidence for understanding the neuropathology underlying ALS.
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Affiliation(s)
- Xujing Ma
- Department of Medical Technology, Cangzhou Medical College, Cangzhou, China
| | - Fengmei Lu
- The Clinical Hospital of Chengdu Brain Science Institute, Chengdu, China.,MOE Key Lab for Neuroinformation, School of life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Heng Chen
- School of Medicine, Guizhou University, Guiyang, China
| | - Caihong Hu
- Department of Medical Technology, Cangzhou Medical College, Cangzhou, China
| | - Jiao Wang
- Department of Medical Technology, Cangzhou Medical College, Cangzhou, China
| | - Sheng Zhang
- Department of Medical Technology, Cangzhou Medical College, Cangzhou, China
| | - Shuqin Zhang
- Department of Medical Technology, Cangzhou Medical College, Cangzhou, China
| | - Guiran Yang
- Department of Medical Technology, Cangzhou Medical College, Cangzhou, China
| | - Jiuquan Zhang
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing, China.,Key Laboratory for Biorheological Science and Technology of Ministry of Education, Chongqing University, Chongqing, China.,Chongqing Cancer Institute, Chongqing, China.,Chongqing Cancer Hospital, Chongqing, China
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11
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The Impact of Robotic Rehabilitation on the Motor System in Neurological Diseases. A Multimodal Neurophysiological Approach. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17186557. [PMID: 32916890 PMCID: PMC7557539 DOI: 10.3390/ijerph17186557] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 08/20/2020] [Accepted: 08/27/2020] [Indexed: 12/13/2022]
Abstract
Motor disability is a key feature of many neurological diseases, influencing the social roles of affected patients and their ability to perform daily life activities. Current rehabilitation capacities are overwhelmed by the age-related increase of motor dysfunctions seen, for example, in stroke, extrapyramidal or neuromuscular diseases. As the patient to rehabilitation personnel ration increases, robotic solutions might establish the possibility to rapidly satisfy the increasing demand for rehabilitation. This paper presents an inaugural exploratory study which investigates the interchangeability of a novel experimental robotic rehabilitation device system with classical physical therapy, using a multimodal neurophysiological assessment of the motor system—quantitative electroencephalogram (EEG), motor conduction times and turn/amplitude analysis. Preliminary results show no significant difference between the two methods; however, a significant effect of the therapy was found on different pathologies (beneficial for vascular and extrapyramidal, or limited, and only on preventing reduction of joint movements in neuromuscular).
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Pender N, Pinto-Grau M, Hardiman O. Cognitive and behavioural impairment in amyotrophic lateral sclerosis. Curr Opin Neurol 2020; 33:649-654. [PMID: 32833751 DOI: 10.1097/wco.0000000000000862] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
PURPOSE OF REVIEW The current review provides an up to date overview of the nature and progression of the cognitive and behavioural impairment in amyotrophic lateral sclerosis (ALS). Understanding these symptoms has implications for the management of the disease and the design of clinical trials, in addition to the support of patient and caregiver regarding mental capacity and end of life decision-making. RECENT FINDINGS Cognitive and behavioural change in ALS are best characterized as the consequence of extensive network dysfunction. 35-45% of ALS patients present with mild-moderate cognitive impairment and comorbid dementia occurs in approximately 14% of patients, the majority of these meeting diagnostic criteria for frontotemporal dementia (FTD). Cognitive change in ALS manifests most commonly as executive dysfunction and language impairment. Behavioural change in the form of apathy, disinhibition, loss of sympathy and empathy, stereotyped behaviours and dietary changes occur. SUMMARY Cognitive and behavioural impairment is an important feature of ALS, and reflects broad network dysfunction of frontostriatal and frontotemporal systems. Cognition and behaviour should be assessed early in the diagnostic process, and data driven approaches should be developed to enable reliable quantitative outcome assessment suitable for clinical trials.
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Affiliation(s)
- Niall Pender
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin.,Department of Psychology
| | - Marta Pinto-Grau
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin.,Department of Psychology
| | - Orla Hardiman
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin.,Department of Neurology, Beaumont Hospital, Dublin, Ireland
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Aliakbaryhosseinabadi S, Farina D, Mrachacz-Kersting N. Real-time neurofeedback is effective in reducing diversion of attention from a motor task in healthy individuals and patients with amyotrophic lateral sclerosis. J Neural Eng 2020; 17:036017. [PMID: 32375135 DOI: 10.1088/1741-2552/ab909c] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
OBJECTIVE The performance of brain-computer interface (BCI) systems is influenced by the user's mental state, such as attention diversion. In this study, we propose a novel online BCI system able to adapt with variations in the users' attention during real-time movement execution. APPROACH Electroencephalography signals were recorded from healthy participants and patients with Amyotrophic Lateral Sclerosis while attention to the target task (a dorsiflexion movement) was drifted using an auditory oddball task. For each participant, the selected channels, classifiers and features from a training data set were used in the online phase to predict the attention status. MAIN RESULTS For both healthy controls and patients, feedback to the user on attentional status reduced the amount of attention diversion. SIGNIFICANCE The findings presented here demonstrate successful monitoring of the users' attention in a fully online BCI system, and further, that real-time neurofeedback on the users' attention state can be implemented to focus the attention of the user back onto the main task.
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McMackin R, Dukic S, Costello E, Pinto-Grau M, Fasano A, Buxo T, Heverin M, Reilly R, Muthuraman M, Pender N, Hardiman O, Nasseroleslami B. Localization of Brain Networks Engaged by the Sustained Attention to Response Task Provides Quantitative Markers of Executive Impairment in Amyotrophic Lateral Sclerosis. Cereb Cortex 2020; 30:4834-4846. [PMID: 32318719 PMCID: PMC7391267 DOI: 10.1093/cercor/bhaa076] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Revised: 02/27/2020] [Accepted: 02/28/2020] [Indexed: 12/13/2022] Open
Abstract
Objective: To identify cortical regions engaged during the sustained attention to response task (SART) and characterize changes in their activity associated with the neurodegenerative condition amyotrophic lateral sclerosis (ALS). Methods: High-density electroencephalography (EEG) was recorded from 33 controls and 23 ALS patients during a SART paradigm. Differences in associated event-related potential peaks were measured for Go and NoGo trials. Sources active during these peaks were localized, and ALS-associated differences were quantified. Results: Go and NoGo N2 and P3 peak sources were localized to the left primary motor cortex, bilateral dorsolateral prefrontal cortex (DLPFC), and lateral posterior parietal cortex (PPC). NoGo trials evoked greater bilateral medial PPC activity during N2 and lesser left insular, PPC and DLPFC activity during P3. Widespread cortical hyperactivity was identified in ALS during P3. Changes in the inferior parietal lobule and insular activity provided very good discrimination (AUROC > 0.75) between patients and controls. Activation of the right precuneus during P3 related to greater executive function in ALS, indicative of a compensatory role. Interpretation: The SART engages numerous frontal and parietal cortical structures. SART–EEG measures correlate with specific cognitive impairments that can be localized to specific structures, aiding in differential diagnosis.
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Affiliation(s)
- Roisin McMackin
- Academic Unit of Neurology, Trinity College Dublin, The University of Dublin, Dublin, D02 R590, Ireland
| | - Stefan Dukic
- Academic Unit of Neurology, Trinity College Dublin, The University of Dublin, Dublin, D02 R590, Ireland.,Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands
| | - Emmet Costello
- Academic Unit of Neurology, Trinity College Dublin, The University of Dublin, Dublin, D02 R590, Ireland
| | - Marta Pinto-Grau
- Academic Unit of Neurology, Trinity College Dublin, The University of Dublin, Dublin, D02 R590, Ireland.,Beaumont Hospital Dublin, Department of Psychology, Dublin 9, Dublin, Ireland
| | - Antonio Fasano
- Academic Unit of Neurology, Trinity College Dublin, The University of Dublin, Dublin, D02 R590, Ireland
| | - Teresa Buxo
- Academic Unit of Neurology, Trinity College Dublin, The University of Dublin, Dublin, D02 R590, Ireland
| | - Mark Heverin
- Academic Unit of Neurology, Trinity College Dublin, The University of Dublin, Dublin, D02 R590, Ireland
| | - Richard Reilly
- Trinity College Institute of Neuroscience, Trinity College Dublin, The University of Dublin, Dublin 2, Dublin, Ireland.,Trinity Centre for Biomedical Engineering, Trinity College, The University of Dublin, Dublin 2, Dublin, Ireland
| | - Muthuraman Muthuraman
- Biomedical Statistics and Multimodal Signal Processing Unit, Department of Neurology, Johannes-Gutenberg- University Hospital, D55131, Mainz, Germany
| | - Niall Pender
- Academic Unit of Neurology, Trinity College Dublin, The University of Dublin, Dublin, D02 R590, Ireland.,Beaumont Hospital Dublin, Department of Psychology, Dublin 9, Dublin, Ireland
| | - Orla Hardiman
- Academic Unit of Neurology, Trinity College Dublin, The University of Dublin, Dublin, D02 R590, Ireland.,Department of Neurology, Beaumont Hospital Dublin, Dublin 9, Dublin, Ireland
| | - Bahman Nasseroleslami
- Academic Unit of Neurology, Trinity College Dublin, The University of Dublin, Dublin, D02 R590, Ireland
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15
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McMackin R, Muthuraman M, Groppa S, Babiloni C, Taylor JP, Kiernan MC, Nasseroleslami B, Hardiman O. Measuring network disruption in neurodegenerative diseases: New approaches using signal analysis. J Neurol Neurosurg Psychiatry 2019; 90:1011-1020. [PMID: 30760643 PMCID: PMC6820156 DOI: 10.1136/jnnp-2018-319581] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2018] [Revised: 01/21/2019] [Accepted: 01/21/2019] [Indexed: 12/12/2022]
Abstract
Advanced neuroimaging has increased understanding of the pathogenesis and spread of disease, and offered new therapeutic targets. MRI and positron emission tomography have shown that neurodegenerative diseases including Alzheimer's disease (AD), Lewy body dementia (LBD), Parkinson's disease (PD), frontotemporal dementia (FTD), amyotrophic lateral sclerosis (ALS) and multiple sclerosis (MS) are associated with changes in brain networks. However, the underlying neurophysiological pathways driving pathological processes are poorly defined. The gap between what imaging can discern and underlying pathophysiology can now be addressed by advanced techniques that explore the cortical neural synchronisation, excitability and functional connectivity that underpin cognitive, motor, sensory and other functions. Transcranial magnetic stimulation can show changes in focal excitability in cortical and transcortical motor circuits, while electroencephalography and magnetoencephalography can now record cortical neural synchronisation and connectivity with good temporal and spatial resolution.Here we reflect on the most promising new approaches to measuring network disruption in AD, LBD, PD, FTD, MS, and ALS. We consider the most groundbreaking and clinically promising studies in this field. We outline the limitations of these techniques and how they can be tackled and discuss how these novel approaches can assist in clinical trials by predicting and monitoring progression of neurophysiological changes underpinning clinical symptomatology.
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Affiliation(s)
- Roisin McMackin
- Academic Unit of Neurology, Trinity College Dublin, the University of Dublin, Dublin, Ireland
| | - Muthuraman Muthuraman
- Department of Neurology, Universitätsmedizin der Johannes Gutenberg-Universität Mainz, Mainz, Germany
| | - Sergiu Groppa
- Department of Neurology, Universitätsmedizin der Johannes Gutenberg-Universität Mainz, Mainz, Germany
| | - Claudio Babiloni
- Dipartimento di Fisiologia e Farmacologia "Vittorio Erspamer", Università degli Studi di Roma "La Sapienza", Roma, Italy
- Istituto di Ricovero e Cura San Raffaele Cassino, Cassino, Italy
| | - John-Paul Taylor
- Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK
| | - Matthew C Kiernan
- Brain & Mind Centre, University of Sydney, Sydney, Sydney, Australia
- Institute of Clinical Neurosciences, Royal Prince Alfred Hospital, Sydney, Sydney, Australia
| | - Bahman Nasseroleslami
- Academic Unit of Neurology, Trinity College Dublin, the University of Dublin, Dublin, Ireland
| | - Orla Hardiman
- Academic Unit of Neurology, Trinity College Dublin, the University of Dublin, Dublin, Ireland
- Beaumont Hospital, Dublin, Ireland
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16
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Dukic S, McMackin R, Buxo T, Fasano A, Chipika R, Pinto-Grau M, Costello E, Schuster C, Hammond M, Heverin M, Coffey A, Broderick M, Iyer PM, Mohr K, Gavin B, Pender N, Bede P, Muthuraman M, Lalor EC, Hardiman O, Nasseroleslami B. Patterned functional network disruption in amyotrophic lateral sclerosis. Hum Brain Mapp 2019; 40:4827-4842. [PMID: 31348605 PMCID: PMC6852475 DOI: 10.1002/hbm.24740] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Revised: 07/01/2019] [Accepted: 07/17/2019] [Indexed: 12/11/2022] Open
Abstract
Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease primarily affecting motor function, with additional evidence of extensive nonmotor involvement. Despite increasing recognition of the disease as a multisystem network disorder characterised by impaired connectivity, the precise neuroelectric characteristics of impaired cortical communication remain to be fully elucidated. Here, we characterise changes in functional connectivity using beamformer source analysis on resting‐state electroencephalography recordings from 74 ALS patients and 47 age‐matched healthy controls. Spatiospectral characteristics of network changes in the ALS patient group were quantified by spectral power, amplitude envelope correlation (co‐modulation) and imaginary coherence (synchrony). We show patterns of decreased spectral power in the occipital and temporal (δ‐ to β‐band), lateral/orbitofrontal (δ‐ to θ‐band) and sensorimotor (β‐band) regions of the brain in patients with ALS. Furthermore, we show increased co‐modulation of neural oscillations in the central and posterior (δ‐, θ‐ and γl‐band) and frontal (δ‐ and γl‐band) regions, as well as decreased synchrony in the temporal and frontal (δ‐ to β‐band) and sensorimotor (β‐band) regions. Factorisation of these complex connectivity patterns reveals a distinct disruption of both motor and nonmotor networks. The observed changes in connectivity correlated with structural MRI changes, functional motor scores and cognitive scores. Characteristic patterned changes of cortical function in ALS signify widespread disease‐associated network disruption, pointing to extensive dysfunction of both motor and cognitive networks. These statistically robust findings, that correlate with clinical scores, provide a strong rationale for further development as biomarkers of network disruption for future clinical trials.
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Affiliation(s)
- Stefan Dukic
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Dublin, Ireland.,Department of Neurology, University Medical Centre Utrecht Brain Centre, Utrecht University, Utrecht, The Netherlands
| | - Roisin McMackin
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Dublin, Ireland
| | - Teresa Buxo
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Dublin, Ireland
| | - Antonio Fasano
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Dublin, Ireland
| | - Rangariroyashe Chipika
- Computational Neuroimaging Group, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Dublin, Ireland
| | - Marta Pinto-Grau
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Dublin, Ireland
| | - Emmet Costello
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Dublin, Ireland
| | - Christina Schuster
- Computational Neuroimaging Group, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Dublin, Ireland
| | - Michaela Hammond
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Dublin, Ireland
| | - Mark Heverin
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Dublin, Ireland
| | - Amina Coffey
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Dublin, Ireland
| | - Michael Broderick
- Trinity Centre for Bioengineering, Trinity College Dublin, University of Dublin, Dublin, Ireland
| | - Parameswaran M Iyer
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Dublin, Ireland
| | - Kieran Mohr
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Dublin, Ireland
| | - Brighid Gavin
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Dublin, Ireland
| | - Niall Pender
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Dublin, Ireland
| | - Peter Bede
- Computational Neuroimaging Group, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Dublin, Ireland
| | - Muthuraman Muthuraman
- Movement disorders and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing Unit, Department of Neurology, Johannes-Gutenberg-University Hospital, Mainz, Germany
| | - Edmund C Lalor
- Trinity Centre for Bioengineering, Trinity College Dublin, University of Dublin, Dublin, Ireland.,Trinity College Institute of Neuroscience, Trinity College Dublin, University of Dublin, Dublin, Ireland.,Department of Biomedical Engineering and Department of Neuroscience, University of Rochester, Rochester, New York
| | - Orla Hardiman
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Dublin, Ireland.,Trinity College Institute of Neuroscience, Trinity College Dublin, University of Dublin, Dublin, Ireland.,Department of Neurology, Beaumont Hospital, Dublin, Ireland
| | - Bahman Nasseroleslami
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Dublin, Ireland
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