Imaging the pathoanatomy of amyotrophic lateral sclerosis in vivo: targeting a propagation-based biological marker

Kassubek, Jan; Müller, Hans‐Peter; Tredici, Kelly Del; Lulé, Dorothée; Gorges, Martin; Braak, Heiko; Ludolph, Albert C.

doi:10.1136/jnnp-2017-316365

Cited by 78 publications

(164 citation statements)

References 38 publications

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“…Validating our simulated impairment using the later stage empirical impairment of individual patients, our model offers a new network‐based perspective on ALS as a progressive disease. The impairment level of certain brain regions has been shown to be closely related to clinical outcome measures . We demonstrated that the simulated aggregation levels correlate with empirical impairment at the level of brain regions and also with future clinical UMN burden.…”

Section: Discussionmentioning

confidence: 84%

“…Furthermore, we demonstrated a consistency between the microscopic pathological staging in ALS and its representation on the macroscopic level of brain regions. This overlap may potentially facilitate tracking disease spread and categorizing patients into disease stages in vivo based on MRI . This result brings the 2 fields of pathological staging and in vivo MRI staging closer together as a crucial step toward combining all available patient information to understand ALS disease progression.…”

Section: Discussionmentioning

confidence: 99%

“…Considering that histopathology is limited to the characterization of postmortem tissues in ALS, magnetic resonance imaging (MRI) measurements have proven their potential to be applied as a biomarker for tracking disease progression and staging in vivo . Modeling the disease spreading mechanism in ALS is the next step toward predicting the observed empirical impairment.…”

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confidence: 99%

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Connectome‐Based Propagation Model in Amyotrophic Lateral Sclerosis

Meier

Burgh

Nitert

et al. 2020

Annals of Neurology

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Objective: Clinical trials in amyotrophic lateral sclerosis (ALS) continue to rely on survival or functional scales as endpoints, despite the emergence of quantitative biomarkers. Neuroimaging-based biomarkers in ALS have been shown to detect ALS-associated pathology in vivo, although anatomical patterns of disease spread are poorly characterized. The objective of this study is to simulate disease propagation using network analyses of cerebral magnetic resonance imaging (MRI) data to predict disease progression. Methods: Using brain networks of ALS patients (n = 208) and matched controls across longitudinal time points, network-based statistics unraveled progressive network degeneration originating from the motor cortex and expanding in a spatiotemporal manner. We applied a computational model to the MRI scan of patients to simulate this progressive network degeneration. Simulated aggregation levels at the group and individual level were validated with empirical impairment observed at later time points of white matter and clinical decline using both internal and external datasets. Results: We observe that computer-simulated aggregation levels mimic true disease patterns in ALS patients. Simulated patterns of involvement across cortical areas show significant overlap with the patterns of empirically impaired brain regions on later scans, at both group and individual levels. These findings are validated using an external longitudinal dataset of 30 patients. Interpretation: Our results are in accordance with established pathological staging systems and may have implications for patient stratification in future clinical trials. Our results demonstrate the utility of computational models in ALS to predict disease progression and underscore their potential as a prognostic biomarker.

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Section: Discussionmentioning

confidence: 84%

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

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Connectome‐Based Propagation Model in Amyotrophic Lateral Sclerosis

Meier

Burgh

Nitert

et al. 2020

Annals of Neurology

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“…MRI studies have consistently shown motor and extra-motor grey matter atrophy (Murphy, Henry, & Lomen-Hoerth, 2007;Omer et al, 2017) and correlation between motor system pathology with ALSFRS-R (Bede et al, 2013;Kassubek et al, 2018;Schmidt et al, 2014;Walhout et al, 2015). Consequently, we first sought to correlate the alterations in connectivity within motor and frontal networks (Kay, Lesser, & Coltheart, 1992) and the Boston Naming test (Graves, Bezeau, Fogarty, & Blair, 2004).…”

Section: Correlates Of Eeg With Mri Neuropsychology and Disease Sementioning

confidence: 99%

Patterned functional network disruption in amyotrophic lateral sclerosis

Dukic

McMackin

Buxó

et al. 2019

Human Brain Mapping

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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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“…Diffusion MRI techniques, such as diffusion tensor imaging (DTI) have been established to study integrity of neuronal tracts in vivo in the human brain. DTI studies found alterations of fiber tract integrity in neurodegenerative diseases like Alzheimer's disease and ALS (18)(19)(20)(21), including callosal involvement (22)(23)(24). An observational study found a consistent reduction in fractional anisotropy in the corpus callosum of ALS patients, extending rostrally, and bilaterally to the region of the primary motor cortices (23).…”

Section: Introductionmentioning

confidence: 99%

Mirror Movements in Amyotrophic Lateral Sclerosis: A Combined Study Using Diffusion Tensor Imaging and Transcranial Magnetic Stimulation

et al. 2020

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Objective: Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disorder predominantly affecting the motor system. In a number of patients, mirror movements (MMs) suggest involvement of transcallosal fiber tracts in conjunction with upper motor neuron involvement. The aim of the study was to elucidate functional and structural alterations of callosal integrity in ALS patients with MMs.Methods: Nineteen patients with ALS displaying MMs and 20 controls underwent clinical assessment, transcranial magnetic stimulation (TMS), and diffusion tensor imaging (DTI). TBSS (tract based spatial statistics) was performed. We investigated ipsilateral silent period (iSP) as a measure of transcallosal inhibition, and diffusion changes in the corpus callosum and corticospinal tract (CST) as measure of structural integrity.Results: In ALS patients TMS revealed a longer mean iSP latency than controls. Twelve ALS patients (63.2%) showed loss of iSP, but none of the controls. Using region of interest analysis, fractional anisotropy (FA) values of the CST were significantly lower in ALS patients compared with controls, but diffusion parameters of the corpus callosum did not differ between patients and controls. The lack of diffusion changes in the corpus callosum was confirmed in whole brain tract based statistics, assessing FA as well as mean, radial, and axial diffusivity. There was a significant negative correlation between resting motor threshold and FA values of the CST, but not between iSP and FA of the corpus callosum.Conclusion: In conclusion the study failed to show microstructural changes in the corpus callosum in conjunction with MMs. One possible reason may be that functional disturbance of transcallosal pathways precede microstructural changes in the corpus callosum.

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Imaging the pathoanatomy of amyotrophic lateral sclerosis in vivo: targeting a propagation-based biological marker

Cited by 78 publications

References 38 publications

Connectome‐Based Propagation Model in Amyotrophic Lateral Sclerosis

Connectome‐Based Propagation Model in Amyotrophic Lateral Sclerosis

Patterned functional network disruption in amyotrophic lateral sclerosis

Mirror Movements in Amyotrophic Lateral Sclerosis: A Combined Study Using Diffusion Tensor Imaging and Transcranial Magnetic Stimulation

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