Corrigendum to: Localization of Brain Networks Engaged by the Sustained Attention to Response Task Provides Quantitative Markers of Executive Impairment in Amyotrophic Lateral Sclerosis

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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Changes of Resting State Brain Networks in Amyotrophic Lateral Sclerosis

NeuroImage ◽

10.1016/s1053-8119(09)70061-8 ◽

2009 ◽

Vol 47 ◽

pp. S46

Author(s):

B Mohammadi ◽

K Kollewe ◽

A Samii ◽

K Krampfl ◽

R Dengler ◽

...

Keyword(s):

Amyotrophic Lateral Sclerosis ◽

Resting State ◽

Brain Networks ◽

Lateral Sclerosis

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Altered cortical hubs in functional brain networks in amyotrophic lateral sclerosis

Neurological Sciences ◽

10.1007/s10072-015-2319-6 ◽

2015 ◽

Vol 36 (11) ◽

pp. 2097-2104 ◽

Cited By ~ 6

Author(s):

Xujing Ma ◽

Jiuquan Zhang ◽

Youxue Zhang ◽

Heng Chen ◽

Rong Li ◽

...

Keyword(s):

Amyotrophic Lateral Sclerosis ◽

Brain Networks ◽

Functional Brain ◽

Functional Brain Networks ◽

Lateral Sclerosis

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Neurodegeneration of brain networks in the amyotrophic lateral sclerosis–frontotemporal lobar degeneration (ALS–FTLD) continuum: evidence from MRI and MEG studies

CNS Spectrums ◽

10.1017/s109285291700075x ◽

2017 ◽

Vol 23 (6) ◽

pp. 378-387 ◽

Cited By ~ 6

Author(s):

Francesca Trojsi ◽

Pierpaolo Sorrentino ◽

Giuseppe Sorrentino ◽

Gioacchino Tedeschi

Keyword(s):

Amyotrophic Lateral Sclerosis ◽

Frontotemporal Lobar Degeneration ◽

Large Scale ◽

Diffusion Tensor ◽

Imaging Techniques ◽

Brain Networks ◽

Treatment Strategies ◽

Early Onset Dementia ◽

Connectivity Patterns ◽

Lateral Sclerosis

Brain imaging techniques, especially those based on magnetic resonance imaging (MRI) and magnetoencephalography (MEG), have been increasingly applied to study multiple large-scale distributed brain networks in healthy people and neurological patients. With regard to neurodegenerative disorders, amyotrophic lateral sclerosis (ALS), clinically characterized by the predominant loss of motor neurons and progressive weakness of voluntary muscles, and frontotemporal lobar degeneration (FTLD), the second most common early-onset dementia, have been proven to share several clinical, neuropathological, genetic, and neuroimaging features. Specifically, overlapping or mildly diverging brain structural and functional connectivity patterns, mostly evaluated by advanced MRI techniques—such as diffusion tensor and resting-state functional MRI (DT–MRI, RS–fMRI)—have been described comparing several ALS and FTLD populations. Moreover, though only pioneering, promising clues on connectivity patterns in the ALS–FTLD continuum may derive from MEG investigations. We will herein overview the current state of knowledge concerning the most advanced neuroimaging findings associated with clinical and genetic patterns of neurodegeneration across the ALS–FTLD continuum, underlying the possibility that network-based approaches may be useful to develop novel biomarkers of disease for adequately designing and monitoring more appropriate treatment strategies.

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Changes of resting state brain networks in amyotrophic lateral sclerosis

Klinische Neurophysiologie ◽

10.1055/s-0029-1216131 ◽

2009 ◽

Vol 40 (01) ◽

Cited By ~ 1

Author(s):

B Mohammadi ◽

K Kollewe ◽

A Samii ◽

K Krampfl ◽

R Dengler ◽

...

Keyword(s):

Amyotrophic Lateral Sclerosis ◽

Resting State ◽

Brain Networks ◽

Lateral Sclerosis

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Changes of resting state brain networks in amyotrophic lateral sclerosis

Experimental Neurology ◽

10.1016/j.expneurol.2009.01.025 ◽

2009 ◽

Vol 217 (1) ◽

pp. 147-153 ◽

Cited By ~ 143

Author(s):

Bahram Mohammadi ◽

Katja Kollewe ◽

Amir Samii ◽

Klaus Krampfl ◽

Reinhard Dengler ◽

...

Keyword(s):

Amyotrophic Lateral Sclerosis ◽

Resting State ◽

Brain Networks ◽

Lateral Sclerosis

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Strong–Weak Pruning for Brain Network Identification in Connectome-Wide Neuroimaging: Application to Amyotrophic Lateral Sclerosis Disease Stage Characterization

International Journal of Neural Systems ◽

10.1142/s0129065719500072 ◽

2019 ◽

Vol 29 (07) ◽

pp. 1950007 ◽

Cited By ~ 2

Author(s):

Angela Serra ◽

Paola Galdi ◽

Emanuele Pesce ◽

Michele Fratello ◽

Francesca Trojsi ◽

...

Keyword(s):

Amyotrophic Lateral Sclerosis ◽

Brain Networks ◽

Structural Connectivity ◽

Temporal Cortex ◽

Brain Network ◽

Detection Methods ◽

Disease Stage ◽

Centrality Measures ◽

Weak Links ◽

Lateral Sclerosis

Magnetic resonance imaging allows acquiring functional and structural connectivity data from which high-density whole-brain networks can be derived to carry out connectome-wide analyses in normal and clinical populations. Graph theory has been widely applied to investigate the modular structure of brain connections by using centrality measures to identify the “hub” of human connectomes, and community detection methods to delineate subnetworks associated with diverse cognitive and sensorimotor functions. These analyses typically rely on a preprocessing step (pruning) to reduce computational complexity and remove the weakest edges that are most likely affected by experimental noise. However, weak links may contain relevant information about brain connectivity, therefore, the identification of the optimal trade-off between retained and discarded edges is a subject of active research. We introduce a pruning algorithm to identify edges that carry the highest information content. The algorithm selects both strong edges (i.e. edges belonging to shortest paths) and weak edges that are topologically relevant in weakly connected subnetworks. The newly developed “strong–weak” pruning (SWP) algorithm was validated on simulated networks that mimic the structure of human brain networks. It was then applied for the analysis of a real dataset of subjects affected by amyotrophic lateral sclerosis (ALS), both at the early (ALS2) and late (ALS3) stage of the disease, and of healthy control subjects. SWP preprocessing allowed identifying statistically significant differences in the path length of networks between patients and healthy subjects. ALS patients showed a decrease of connectivity between frontal cortex to temporal cortex and parietal cortex and between temporal and occipital cortex. Moreover, degree of centrality measures revealed significantly different hub and centrality scores between patient subgroups. These findings suggest a widespread alteration of network topology in ALS associated with disease progression.

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Automated Acoustic Analysis of Oral Diadochokinesis to Assess Bulbar Motor Involvement in Amyotrophic Lateral Sclerosis

Journal of Speech Language and Hearing Research ◽

10.1044/2019_jslhr-19-00178 ◽

2020 ◽

Vol 63 (1) ◽

pp. 59-73 ◽

Cited By ~ 3

Author(s):

Panying Rong

Keyword(s):

Amyotrophic Lateral Sclerosis ◽

Acoustic Analysis ◽

Speaking Rate ◽

Disease Stage ◽

Healthy Controls ◽

Tongue Movement ◽

Major Barrier ◽

Syllable Repetition ◽

Oral Diadochokinesis ◽

Lateral Sclerosis

Purpose The purpose of this article was to validate a novel acoustic analysis of oral diadochokinesis (DDK) in assessing bulbar motor involvement in amyotrophic lateral sclerosis (ALS). Method An automated acoustic DDK analysis was developed, which filtered out the voice features and extracted the envelope of the acoustic waveform reflecting the temporal pattern of syllable repetitions during an oral DDK task (i.e., repetitions of /tɑ/ at the maximum rate on 1 breath). Cycle-to-cycle temporal variability (cTV) of envelope fluctuations and syllable repetition rate (sylRate) were derived from the envelope and validated against 2 kinematic measures, which are tongue movement jitter (movJitter) and alternating tongue movement rate (AMR) during the DDK task, in 16 individuals with bulbar ALS and 18 healthy controls. After the validation, cTV, sylRate, movJitter, and AMR, along with an established clinical speech measure, that is, speaking rate (SR), were compared in their ability to (a) differentiate individuals with ALS from healthy controls and (b) detect early-stage bulbar declines in ALS. Results cTV and sylRate were significantly correlated with movJitter and AMR, respectively, across individuals with ALS and healthy controls, confirming the validity of the acoustic DDK analysis in extracting the temporal DDK pattern. Among all the acoustic and kinematic DDK measures, cTV showed the highest diagnostic accuracy (i.e., 0.87) with 80% sensitivity and 94% specificity in differentiating individuals with ALS from healthy controls, which outperformed the SR measure. Moreover, cTV showed a large increase during the early disease stage, which preceded the decline of SR. Conclusions This study provided preliminary validation of a novel automated acoustic DDK analysis in extracting a useful measure, namely, cTV, for early detection of bulbar ALS. This analysis overcame a major barrier in the existing acoustic DDK analysis, which is continuous voicing between syllables that interferes with syllable structures. This approach has potential clinical applications as a novel bulbar assessment.

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