Neurophysiological signatures of Alzheimer’s disease and frontotemporal lobar degeneration: pathology versus phenotype

Sami, Saber; Williams, Nitin; Hughes, Laura E.; Cope, Thomas E.; Rittman, Timothy; Coyle‐Gilchrist, Ian; Henson, Richard N.; Rowe, James B.

doi:10.1093/brain/awy180

Cited by 63 publications

(61 citation statements)

References 75 publications

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“…Artifact-related components were removed. MEG data were segmented into 4-second epochs and filtered using a 2000th order FIR band-pass filter with a Hanning window into five bands (with 2 seconds of real data padding added either side): for source analysis: broad band (2-45 Hz), and for connectivity analysis: theta (4-8 Hz), alpha (8-12 Hz), beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45). The data were coregistered to the T1-weighted MRI and the forward model calculated using a realistic single shell head [11].…”

Section: Analysis 3: Source-level Power Analyses and Functional Connementioning

confidence: 99%

Biomagnetic biomarkers for dementia: A pilot multicentre study with a recommended methodological framework for magnetoencephalography

Hughes

Henson

Pereda

et al. 2019

Alz & Dem Diag Ass & Dis Mo

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Introduction An increasing number of studies are using magnetoencephalography (MEG) to study dementia. Here we define a common methodological framework for MEG resting-state acquisition and analysis to facilitate the pooling of data from different sites. Methods Two groups of patients with mild cognitive impairment (MCI, n = 84) and healthy controls (n = 84) were combined from three sites, and site and group differences inspected in terms of power spectra and functional connectivity. Classification accuracy for MCI versus controls was compared across three different types of MEG analyses, and compared with classification based on structural MRI. Results The spectral analyses confirmed frequency-specific differences in patients with MCI, both in power and connectivity patterns, with highest classification accuracy from connectivity. Critically, site acquisition differences did not dominate the results. Discussion This work provides detailed protocols and analyses that are sensitive to cognitive impairment, and that will enable standardized data sharing to facilitate large-scale collaborative projects.

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Section: Analysis 3: Source-level Power Analyses and Functional Connementioning

confidence: 99%

Biomagnetic biomarkers for dementia: A pilot multicentre study with a recommended methodological framework for magnetoencephalography

Hughes

Henson

Pereda

et al. 2019

Alz & Dem Diag Ass & Dis Mo

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“…MEG has only been deployed recently to investigate PPA (e.g., Kielar et al, 2018), but has already shown its potential as a tool to study the neurophysiological signatures of network-level alterations due to neurodegenerative disorders. It can be instrumental in identifying syndromespecific changes in the spectral properties of oscillatory responses (Ranasinghe et al, 2017;Sami et al, 2018). As suggested by task-free fMRI evidence, these functional alterations might precede structural ones and may be key for early diagnosis (Bonakdarpour et al, 2017).…”

Section: The Latency Of the Dorsal Activation Indicates Slow Serial mentioning

confidence: 99%

Taking the sub-lexical route: brain dynamics of reading in the semantic variant of Primary Progressive Aphasia

Borghesani

Hinkley

Ranasinghe

et al. 2019

Preprint

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368 words Introduction: 1040 words Discussion: 2135 words Whole paper: 5769 words Tables: 4 Figures: 4 50-word Abbreviated Summary:Borghesani et al. investigate brain dynamics during irregular word reading using magnetoencephalographic imaging in patients with semantic variant of primary progressive aphasia. Due to anterior temporal lobe neurodegeneration, patients rely more on dorsal, occipitoparietal brain regions -providing novel evidence on the interplay between ventral and dorsal reading routes. AbstractReading aloud requires mapping an orthographic form to a phonological one. The mapping process relies on sub-lexical statistical regularities (e.g., "oo" to |uː|) or on learned lexical associations between a specific visual form and a series of sounds (e.g., yacht to /jɑt/). Computational, neuroimaging, and neuropsychological evidence suggest that sub-lexical and lexical processes rely on partially distinct neural substrates: a dorsal (occipito-parietal) and a ventral (occipito-temporal) route, respectively.Here, we investigated the spatiotemporal features of sub-lexical processes capitalizing the time resolution of magnetoencephalography and the unique clinical model offered by patients with semantic variant of Primary Progressive Aphasia (svPPA). Behaviorally, svPPA patients manifest marked lexico-semantic impairments including difficulties in reading words with exceptional orthographic to phonological correspondance (irregular words). Moreover, they present with focal neurodegeneration in the anterior temporal lobe (ATL), affecting primarily the ventral, occipitotemporal, lexical route. Therefore, this clinical population allows for testing of specific hypotheses on the neural implementation of the dual-route model for reading, such as whether damage to one route can be compensated by over-reliance on the other. To this end, we reconstructed and analyzed time-resolved whole-brain activity in 12 svPPA patients and 12 healthy age-matched controls while reading irregular words (e.g., yacht) and pseudowords (e.g., pook).Consistent with previous findings that the dorsal route is involved in sub-lexical processes, in control participants we observed enhanced neural activity over dorsal occipito-parietal cortices for pseudowords, when compared to irregular words. This activation was manifested in the betaband (13-20 Hz), ramping up slowly over 500 ms after stimulus onset and peaking at ~800 ms, around response selection and production. This temporal pattern of neural activity was not observed in svPPA patients. Furthermore, a direct comparison of neural activity between patients and controls revealed a dorsal spatiotemporal cluster during irregular word reading. These findings suggest that the sub-lexical route is involved in processing both irregular and pseudo-words in svPPA.Together these results provide further evidence supporting a dual-route model for reading aloud -mediated by the interplay between lexical (ventral) and sub-lexical (dorsal) neuro-cognitive systems. When the lexical route is damaged, as in ...

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“…, to capture movement-intentions (Wolpaw et al, 1991;Lotte et al, 2007;Tangermann et al, 2008). For biomarkers applications, the focus is on predicting medical diagnosis and other clinical endpoints Sami et al, 2018;Mazaheri et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Predictive regression modeling with MEG/EEG: from source power to signals and cognitive states

Sabbagh

Ablin

Varoquaux

et al. 2019

Preprint

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Predicting biomedical outcomes from Magnetoencephalography and Electroencephalography (M/EEG) is central to applications like decoding, brain-computer-interfaces (BCI) or biomarker development and is facilitated by supervised machine learning. Yet most of the literature is concerned with within-subject classification. Here, we focus on predicting continuous outcomes from M/EEG signal power across subjects. Considering different generative mechanisms for M/EEG signals and the biomedical outcome, we propose statistically-consistent predictive models that avoid sourcereconstruction based on the covariance as representation. Our mathematical analysis and ground truth simulations demonstrated that consistent parameter estimation can be obtained with Source Power Comodulation (SPoC) supervised spatial filtering or by embedding with Riemannian geometry. Additional simulations revealed that Riemannian methods were more robust to model violations, in particular geometric distortions induced by individual anatomy. To estimate the relative contribution of brain dynamics and anatomy to prediction performance, we propose a novel model inspection procedure based on biophysical forward modeling. Applied to cross-subject prediction, the analysis revealed that the Riemannian model better exploited anatomical information while sensitivity to brain dynamics was similar across methods. We then probed the robustness of the models across different data cleaning options. Environmental denoising was globally important but Riemannian models were strikingly robust and continued performing even without preprocessing. Our results suggest each method has its niche: SPoC is practical for within-subject prediction while the Riemannian model may enable simple end-to-end learning.

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Neurophysiological signatures of Alzheimer’s disease and frontotemporal lobar degeneration: pathology versus phenotype

Cited by 63 publications

References 75 publications

Biomagnetic biomarkers for dementia: A pilot multicentre study with a recommended methodological framework for magnetoencephalography

Biomagnetic biomarkers for dementia: A pilot multicentre study with a recommended methodological framework for magnetoencephalography

Taking the sub-lexical route: brain dynamics of reading in the semantic variant of Primary Progressive Aphasia

Predictive regression modeling with MEG/EEG: from source power to signals and cognitive states

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