0000000000341936

AUTHOR

Edmund C. Lalor

0000-0002-2498-6631

showing 3 related works from this author

Dysfunction of attention switching networks in amyotrophic lateral sclerosis

2019

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 investigat…

MaleMismatch negativitySource localisationEEG ElectroencephalographyMismatch negativityNetworkElectroencephalographylcsh:RC346-429PET Positron emission tomographyCognition0302 clinical medicineC9orf72AttentionEEGAUROC Area under receiver operating characteristic curveAmyotrophic lateral sclerosisAged 80 and overmedicine.diagnostic_test05 social sciencesCognitive flexibilityBrainRegular ArticleElectroencephalographyCognitionMiddle AgedSTG Superior temporal gyrusNeurologyMTG Mid temporal gyrusDLPFC Dorsolateral prefrontal cortexlcsh:R858-859.7FemaleLCMV Linearly constrained minimum varianceIFG Inferior frontal gyrusAdultCognitive Neurosciencelcsh:Computer applications to medicine. Medical informatics050105 experimental psychologyCWIT Colour-word interference test03 medical and health sciencesfMRI Functional magnetic resonance imagingMEG MagnetoencephalographymedicineMMN Mismatch negativityHumans0501 psychology and cognitive sciencesRadiology Nuclear Medicine and imagingLS Amyotrophic Lateral SclerosisAAL Automated Anatomical Labellinglcsh:Neurology. Diseases of the nervous systemAEP Auditory evoked potentialAgedbusiness.industryAmyotrophic Lateral SclerosisIQR Interquartile rangeNeurophysiologyqEEG Quantitative EEGmedicine.diseaseNeurology (clinical)Nerve NetFunctional magnetic resonance imagingbusinessNeuroscience030217 neurology & neurosurgeryeLORETA Exact low-resolution brain electromagnetic tomographyNeuroImage: Clinical
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Patterned functional network disruption in amyotrophic lateral sclerosis

2019

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 quantifi…

AdultMaleamyotrophic lateral sclerosisNeuropsychological TestsElectroencephalographyBiology050105 experimental psychologyFunctional networksCorrelationmotor neurone disease03 medical and health sciencesCognition0302 clinical medicinemedicineHumanssource localisation0501 psychology and cognitive sciencesRadiology Nuclear Medicine and imagingEEGTheta RhythmAmyotrophic lateral sclerosisresting stateResearch ArticlesAgedCerebral CortexBrain MappingRadiological and Ultrasound TechnologyResting state fMRImedicine.diagnostic_testFunctional connectivityfunctional connectivity05 social sciencesElectroencephalographyCognitionMiddle Agedmedicine.diseaseMagnetic Resonance ImagingDelta RhythmNeurologyFemaleNeurology (clinical)Nerve NetAnatomyBeta RhythmNeuroscienceMotor neurone diseasePsychomotor Performance030217 neurology & neurosurgeryResearch ArticleHuman Brain Mapping
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Non-Parametric Rank Statistics for Spectral Power and Coherence

2019

AbstractDespite advances in multivariate spectral analysis of neural signals, the statistical inference of measures such as spectral power and coherence in practical and real-life scenarios remains a challenge. The non-normal distribution of the neural signals and presence of artefactual components make it difficult to use the parametric methods for robust estimation of measures or to infer the presence of specific spectral components above the chance level. Furthermore, the bias of the coherence measures and their complex statistical distributions are impediments in robust statistical comparisons between 2 different levels of coherence. Non-parametric methods based on the median of auto-/c…

Multivariate statisticsbusiness.industryComputer scienceStatistical inferenceNonparametric statisticsProbability distributionCoherence (signal processing)Spectral analysisDigital signalPattern recognitionArtificial intelligencebusinessCoherence (physics)
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