Functional MRI in Patients with the C9ORF72 Expansion associate Frontotemporal Dementia

Rytty, Riikka; Nikkinen, Juha; Noora, Suhonen; Moilanen, Virpi; E, Renton Alan; J, Traynor Bryan; Tervonen, Osmo; Vesa, Kiviniemi; M, Remes Anne

doi:10.4172/2168-9547.1000117

Cited by 3 publications

(1 citation statement)

References 48 publications

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“…In our previous FC analysis we took gray matter atrophy into account by using gray matter as a spatial covariate in dual regression analysis of FC differences in bvFTD (Rytty et al, 2013, 2014). This may not be enough since loss of gray matter may induce for more of partial volume effects of CSF.…”

Section: Introductionmentioning

confidence: 99%

The Effect of Gray Matter ICA and Coefficient of Variation Mapping of BOLD Data on the Detection of Functional Connectivity Changes in Alzheimer’s Disease and bvFTD

Tuovinen

Rytty

Moilanen

et al. 2017

Front. Hum. Neurosci.

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Resting-state fMRI results in neurodegenerative diseases have been somewhat conflicting. This may be due to complex partial volume effects of CSF in BOLD signal in patients with brain atrophy. To encounter this problem, we used a coefficient of variation (CV) map to highlight artifacts in the data, followed by analysis of gray matter voxels in order to minimize brain volume effects between groups. The effects of these measures were compared to whole brain ICA dual regression results in Alzheimer’s disease (AD) and behavioral variant frontotemporal dementia (bvFTD). 23 AD patients, 21 bvFTD patients and 25 healthy controls were included. The quality of the data was controlled by CV mapping. For detecting functional connectivity (FC) differences whole brain ICA (wbICA) and also segmented gray matter ICA (gmICA) followed by dual regression were conducted, both of which were performed both before and after data quality control. Decreased FC was detected in posterior DMN in the AD group and in the Salience network in the bvFTD group after combining CV quality control with gmICA. Before CV quality control, the decreased connectivity finding was not detectable in gmICA in neither of the groups. Same finding recurred when exclusion was based on randomization. The subjects excluded due to artifacts noticed in the CV maps had significantly lower temporal signal-to-noise ratio than the included subjects. Data quality measure CV is an effective tool in detecting artifacts from resting state analysis. CV reflects temporal dispersion of the BOLD signal stability and may thus be most helpful for spatial ICA, which has a blind spot in spatially correlating widespread artifacts. CV mapping in conjunction with gmICA yields results suiting previous findings both in AD and bvFTD.

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Section: Introductionmentioning

confidence: 99%

The Effect of Gray Matter ICA and Coefficient of Variation Mapping of BOLD Data on the Detection of Functional Connectivity Changes in Alzheimer’s Disease and bvFTD

Tuovinen

Rytty

Moilanen

et al. 2017

Front. Hum. Neurosci.

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The imaging signature of C9orf72 hexanucleotide repeat expansions: implications for clinical trials and therapy development

Shing

McKenna

Siah

et al. 2021

Brain Imaging and Behavior

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Resting state functional connectivity differences between behavioral variant frontotemporal dementia and Alzheimer's disease

Hafkemeijer

Möller

Dopper

et al. 2015

Front. Hum. Neurosci.

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Introduction: Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD) are the most common types of early-onset dementia. Early differentiation between both types of dementia may be challenging due to heterogeneity and overlap of symptoms. Here, we apply resting state functional magnetic resonance imaging (fMRI) to study functional brain connectivity differences between AD and bvFTD. Methods:We used resting state fMRI data of 31 AD patients, 25 bvFTD patients, and 29 controls from two centers specialized in dementia. We studied functional connectivity throughout the entire brain, applying two different analysis techniques, studying network-to-region and region-to-region connectivity. A general linear model approach was used to study group differences, while controlling for physiological noise, age, gender, study center, and regional gray matter volume.Results: Given gray matter differences, we observed decreased network-to-region connectivity in bvFTD between (a) lateral visual cortical network and lateral occipital and cuneal cortex, and (b) auditory system network and angular gyrus. In AD, we found decreased network-to-region connectivity between the dorsal visual stream network and lateral occipital and parietal opercular cortex. Region-to-region connectivity was decreased in bvFTD between superior temporal gyrus and cuneal, supracalcarine, intracalcarine cortex, and lingual gyrus. Conclusion:We showed that the pathophysiology of functional brain connectivity is different between AD and bvFTD. Our findings support the hypothesis that resting state fMRI shows disease-specific functional connectivity differences and is useful to elucidate the pathophysiology of AD and bvFTD. However, the group differences in functional connectivity are less abundant than has been shown in previous studies.

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Functional MRI in Patients with the C9ORF72 Expansion associate Frontotemporal Dementia

Cited by 3 publications

References 48 publications

The Effect of Gray Matter ICA and Coefficient of Variation Mapping of BOLD Data on the Detection of Functional Connectivity Changes in Alzheimer’s Disease and bvFTD

The Effect of Gray Matter ICA and Coefficient of Variation Mapping of BOLD Data on the Detection of Functional Connectivity Changes in Alzheimer’s Disease and bvFTD

The imaging signature of C9orf72 hexanucleotide repeat expansions: implications for clinical trials and therapy development

Resting state functional connectivity differences between behavioral variant frontotemporal dementia and Alzheimer's disease

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