Publication: Neurobiology of Aging

Ece Kocagoncu, Andrew Quinn, Azadeh Firouzian, Elisa Cooper, Andrea Greve, Roger Gunn, Gary Green, Mark W. Woolrich, Richard N.Henson, Simon Lovestone, James B.Rowe

1 August 2020

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Understanding the role of Tau protein aggregation in the pathogenesis of Alzheimer’s disease is critical for the development of new Tau-based therapeutic strategies to slow or prevent dementia. The researchers tested the hypothesis that Tau pathology is associated with functional organization of widespread neurophysiological networks.

They found that higher Tau burden in early Alzheimer’s disease was associated with a shift away from the optimal small-world organization and a more fragmented network in the beta and gamma bands, whereby parieto-occipital areas were disconnected from the anterior parts of the network.

The results support the translational development of neurophysiological “signatures” of Alzheimer’s disease, to understand disease mechanisms in humans and facilitate experimental medicine studies.

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Publication: Journal of Alzheimer’s Disease

Nicolasa Nicastro, Maura Malpetti, Thomas Cope, William Richard Bevan-Jones, Elijah Mak, Luca Passamonti, James B. Rowe, John T. O’Brien

30 June 2020

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The changes of cortical structure in Alzheimer’s disease (AD) and frontotemporal dementia (FTD) are usually described in terms of atrophy. However, neurodegenerative diseases may also affect the complexity of cortical shape, such as the fractal dimension of the brain surface.

In this study, the researchers assessed the regional patterns of cortical thickness and fractal dimension changes in a cross-sectional cohort of patients with AD and FTD.

In addition to the well-established pattern of cortical thinning encompassing temporoparietal regions in AD and frontotemporal areas in FTD, they observed reductions of fractal dimension encompassing cingulate areas and insula for both conditions, but specifically involving orbitofrontal cortex and paracentral gyrus for FTD (FDR p < 0.05). Correlational analyses between fractal dimension and cognition showed that these regions were particularly vulnerable with regards to memory and language impairment, especially in FTD.

While the present study demonstrates globally similar patterns of fractal dimension changes in AD and FTD, the researchers observed distinct cortical complexity correlates of cognitive domains impairment. Further studies are required to assess cortical complexity measures at earlier disease stages (e.g., in prodromal/asymptomatic carriers of FTD-related gene mutations) and determine whether fractal dimension represents a sensitive imaging marker for prevention and diagnostic strategies.

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Publication: Cortex

Thomas E. Cope, Yury Shtyrov, Lucy J. MacGregor, Rachel Holland, Friedemann Pulvermüller, James B. Rowe, Karalyn Patterson

1 May 2020

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In the healthy human brain, the processing of language is strongly lateralised, usually to the left hemisphere, while the processing of complex non-linguistic sounds recruits brain regions bilaterally. Here the researchers asked whether the anterior temporal lobes, strongly implicated in semantic processing, are critical to this special treatment of spoken words. Nine patients with semantic dementia (SD) and fourteen age-matched controls underwent magnetoencephalography and structural MRI.

Source reconstructions confirmed recruitment of right-sided analogues of language regions in SD: atrophy of anterior temporal lobes was associated with increased activity in right temporal pole, middle temporal gyrus, inferior frontal gyrus and supramarginal gyrus.

Overall, the results indicate that anterior temporal lobes are necessary for normal and efficient lateralised processing of word identity by the language network.

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Publication: Brain Communications

Marta M Correia, Timothy Rittman, Christopher L Barnes, Ian T Coyle-Gilchrist, Boyd Ghosh, Laura E Hughes, James B Rowe

27 April 2020

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The early and accurate differential diagnosis of parkinsonian disorders is still a significant challenge for clinicians. In recent years, a number of studies have used magnetic resonance imaging data combined with machine learning and statistical classifiers to successfully differentiate between different forms of Parkinsonism. However, several questions and methodological issues remain, to minimize bias and artefact-driven classification. In this study, the researchers compared different approaches for feature selection, as well as different magnetic resonance imaging modalities, with well-matched patient groups and tightly controlling for data quality issues related to patient motion.

Their cross-validation results suggest that using principal components analysis for feature extraction provides higher classification accuracies when compared to a region-of-interest based approach. However, the differences between the two feature extraction methods were significantly reduced when an independent sample was used for validation, suggesting that the principal components analysis approach may be more vulnerable to overfitting with cross-validation. Both T1-weighted and diffusion magnetic resonance imaging data could be used to successfully differentiate between subject groups, with neither modality outperforming the other across all pairwise comparisons in the cross-validation analysis. However, features obtained from diffusion magnetic resonance imaging data resulted in significantly higher classification accuracies when an independent validation cohort was used.

Overall, the results support the use of statistical classification approaches for differential diagnosis of parkinsonian disorders. However, classification accuracy can be affected by group size, age, sex and movement artefacts. With appropriate controls and out-of-sample cross validation, diagnostic biomarker evaluation including magnetic resonance imaging based classifiers may be an important adjunct to clinical evaluation.

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Publication: Frontiers in Neuroscience

Audrey Low, Elijah Mak, James D. Stefaniak, Maura Malpetti, Nicolas Nicastro, George Savulich, Leonidas Chouliaras, Hugh S. Markus, James B. Rowe and John T. O’Brien

19 March 2020

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The peak width of skeletonized mean diffusivity (PSMD) has been proposed as a fully automated imaging marker of relevance to cerebral small vessel disease (SVD). The researchers assessed PSMD in relation to conventional SVD markers, global measures of neurodegeneration, and cognition.

PSMD was associated with global and regional SVD measures, especially WMH and microbleeds. Dominance analysis demonstrated that among SVD markers, WMH was the strongest predictor of PSMD. Furthermore, PSMD was more closely associated to WMH than with GM and WM volumes.

This new measure appears to be a marker of diffuse brain injury, largely due to vascular pathology, and may be a useful and convenient metric of overall cerebrovascular burden.

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