Language Network Connectivity Increases in Early Alzheimer’s Disease

Background: Language production deficits occur early in the course of Alzheimer’s disease (AD); however, only a few studies have focused on language network’s functional connectivity in mild cognitive impairment (MCI) due to AD. Objective: The current study aims to uncover the extent of language alteration at the MCI stage, at a behavioral and neural level, using univariate and multivariate analyses of structural MRI and resting-state fMRI. Methods: Twenty-four MCI due to AD participants and 24 matched healthy controls underwent a comprehensive language evaluation, a structural T1-3D MRI, and resting-state fMRI. We performed seed-based analyses, using the left inferior frontal gyrus and left posterior temporal gyrus as seeds. Then, we analyzed connectivity between executive control networks and language network in each group. Finally, we used multivariate pattern analyses to test whether the two groups could be distinguished based on the pattern of atrophy within the language network; within the executive control networks, as well as the pattern of functional connectivity within the language network and within the executive control networks. Results: MCI due to AD participants had language impairment during standardized language tasks and connected-speech production. Regarding functional connectivity, univariate analyses were not able to discriminate participants, while multivariate pattern analyses could significantly predict participants’ group. Language network’s functional connectivity could discriminate MCI due to AD participants better than executive control networks. Most notably, they revealed an increased connectivity at the MCI stage, positively correlated with language performance. Conclusion: Multivariate analyses represent a useful tool for investigating the functional and structural (re-)organization of the neural bases of language.

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Language network connectivity increases in prodromal Alzheimer’s disease

10.1101/2020.11.22.393199 ◽

2020 ◽

Author(s):

A. Pistono ◽

M. Senoussi ◽

L. Guerrier ◽

M. Rafiq ◽

M. Gimeno ◽

...

Keyword(s):

Executive Control ◽

Multivariate Analyses ◽

Network Connectivity ◽

Healthy Controls ◽

Prodromal Stage ◽

Control Networks ◽

Prodromal Ad ◽

Connectivity Patterns ◽

Multivariate Pattern ◽

Language Network

AbstractLanguage production deficits occur early in the course of Alzheimer’s disease (AD); however, only few studies have focused on language functional networks in prodromal AD. The current study aims to uncover the extent of language alteration at a prodromal stage, on a behavioral, structural and functional level, using univariate and multivariate analyses. Twenty-four AD participants and 24 matched healthy controls underwent a comprehensive language evaluation, a structural T1-3D MRI and resting-state fMRI. We performed seed-based analyses, using the left inferior frontal gyrus and left posterior temporal gyrus as seeds. Then, we analyzed connectivity between executive control networks and language network in each group. Finally, we used multivariate pattern analyses to test whether the two groups could be distinguished based on the pattern of atrophy within the language network; atrophy within the executive control networks, as well as the pattern of functional connectivity within the language network; and functional connectivity within executive control networks. AD participants had language impairment during standardized language tasks and connected-speech production. Univariate analyses were not able to discriminate participants at this stage, while multivariate pattern analyses could significantly predict the group membership of prodromal patients and healthy controls, both when classifying atrophy patterns or connectivity patterns of the language network. Language functional networks could discriminate AD participants better than executive control networks. Most notably, they revealed an increased connectivity at a prodromal stage. Multivariate analyses represent a useful tool for investigating the functional and structural (re-)organization of the neural bases of language.HighlightsLanguage network connectivity discriminates prodromal AD from healthy controlsLanguage network connectivity increases in prodromal ADAtrophy patterns in the language network do not correlate with connectivity patterns in AD

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Evolutionary optimization in classification of early-MCI patients from healthy controls using graph measures of resting-state fMRI

10.1101/2021.03.04.433989 ◽

2021 ◽

Author(s):

Jafar Zamani ◽

Ali Sadr ◽

Amir-Homayoun Javadi

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Functional Connectivity ◽

Evolutionary Algorithms ◽

Resting State ◽

Optimization Methods ◽

Resting State Fmri ◽

Optimization Method ◽

Effective Parameters

AbstractsIdentifying individuals with early mild cognitive impairment (EMCI) can be an effective strategy for early diagnosis and delay the progression of Alzheimer’s disease (AD). Many approaches have been devised to discriminate those with EMCI from healthy control (HC) individuals. Selection of the most effective parameters has been one of the challenging aspects of these approaches. In this study we suggest an optimization method based on five evolutionary algorithms that can be used in optimization of neuroimaging data with a large number of parameters. Resting-state functional magnetic resonance imaging (rs-fMRI) measures, which measure functional connectivity, have been shown to be useful in prediction of cognitive decline. Analysis of functional connectivity data using graph measures is a common practice that results in a great number of parameters. Using graph measures we calculated 1155 parameters from the functional connectivity data of HC (n=36) and EMCI (n=34) extracted from the publicly available database of the Alzheimer’s disease neuroimaging initiative database (ADNI). These parameters were fed into the evolutionary algorithms to select a subset of parameters for classification of the data into two categories of EMCI and HC using a two-layer artificial neural network. All algorithms achieved classification accuracy of 94.55%, which is extremely high considering single-modality input and low number of data participants. These results highlight potential application of rs-fMRI and efficiency of such optimization methods in classification of images into HC and EMCI. This is of particular importance considering that MRI images of EMCI individuals cannot be easily identified by experts.

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The Identification of Alzheimer’s Disease Using Functional Connectivity Between Activity Voxels in Resting-State fMRI Data

IEEE Journal of Translational Engineering in Health and Medicine ◽

10.1109/jtehm.2020.2985022 ◽

2020 ◽

Vol 8 ◽

pp. 1-11

Author(s):

Yuhu Shi ◽

Weiming Zeng ◽

Jin Deng ◽

Weifang Nie ◽

Yifei Zhang

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Functional Connectivity ◽

Resting State ◽

Resting State Fmri ◽

Fmri Data

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Altered functional connectivity in early Alzheimer's disease: A resting-state fMRI study

Human Brain Mapping ◽

10.1002/hbm.20324 ◽

2007 ◽

Vol 28 (10) ◽

pp. 967-978 ◽

Cited By ~ 467

Author(s):

Kun Wang ◽

Meng Liang ◽

Liang Wang ◽

Lixia Tian ◽

Xinqing Zhang ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Functional Connectivity ◽

Resting State ◽

Resting State Fmri ◽

Fmri Study ◽

Early Alzheimer’S Disease

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Characteristics of the default mode functional connectivity in normal ageing and Alzheimer's disease using resting state fMRI with a combined approach of entropy-based and graph theoretical measurements

NeuroImage ◽

10.1016/j.neuroimage.2014.08.003 ◽

2014 ◽

Vol 101 ◽

pp. 778-786 ◽

Cited By ~ 61

Author(s):

Paule-Joanne Toussaint ◽

Sofiane Maiz ◽

David Coynel ◽

Julien Doyon ◽

Arnaud Messé ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Functional Connectivity ◽

Resting State ◽

Resting State Fmri ◽

Combined Approach ◽

Default Mode ◽

Normal Ageing

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Altered Directed Functional Connectivity of the Hippocampus in Mild Cognitive Impairment and Alzheimer’s Disease: A Resting-State fMRI Study

Frontiers in Aging Neuroscience ◽

10.3389/fnagi.2019.00326 ◽

2019 ◽

Vol 11 ◽

Cited By ~ 5

Author(s):

Jiayue Xue ◽

Hao Guo ◽

Yuan Gao ◽

Xin Wang ◽

Huifang Cui ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Functional Connectivity ◽

Resting State ◽

Resting State Fmri ◽

Fmri Study

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Baseline functional connectivity in resting state networks associated with depression and remission status after 16 weeks of pharmacotherapy: A CAN-BIND Report

10.1101/2021.01.27.21250490 ◽

2021 ◽

Author(s):

Gwen van der Wijk ◽

Jacqueline K. Harris ◽

Stefanie Hassel ◽

Andrew D. Davis ◽

Mojdeh Zamyadi ◽

...

Keyword(s):

Functional Connectivity ◽

Treatment Outcomes ◽

Resting State ◽

Rating Scale ◽

Resting State Fmri ◽

Statistical Technique ◽

Multivariate Statistical ◽

Control Networks ◽

Neural Underpinnings ◽

The Stability

AbstractUnderstanding the neural underpinnings of major depressive disorder (MDD) and its treatment could improve treatment outcomes. While numerous studies have been conducted, findings are variable and large sample replications scarce. We aimed to replicate and extend altered functional connectivity findings in the default mode, salience and cognitive control networks (DMN, SN, and CCN respectively) associated with MDD and pharmacotherapy outcomes in a large, multi-site sample. Resting-state fMRI data were collected from 129 patients and 99 controls through the Canadian Biomarker Integration Network in Depression (CAN-BIND) initiative. Symptoms were assessed with the Montgomery-Åsberg Depression Rating Scale (MADRS). Connectivity was measured as correlations between four seeds (anterior and posterior DMN, SN and CCN) and all other brain voxels across participants. Partial least squares, a multivariate statistical technique, was used to compare connectivity prior to treatment between patients and controls, and between patients reaching remission early (MADRS ≤ 10 within 8 weeks), late (MADRS ≤ 10 within 16 weeks) or not at all. We replicated previous findings of altered connectivity in the DMN, SN and CCN in patients. In addition, baseline connectivity of the anterior/posterior DMN and SN seeds differentiated patients with different treatment outcomes. Weaker connectivity within the anterior DMN and between the anterior DMN and the SN and CCN characterised early remission; stronger connectivity within the SN and weaker connectivity between the SN and the DMN and CCN was related to late remission, of which the weaker SN – anterior DMN connectivity might specifically be associated with remission to dual pharmacotherapy; and connectivity strength between the posterior DMN and cingulate areas distinguished all three groups, with early remitters showing the strongest connections and non-remitters the weakest. The stability of these baseline patient differences was established in the largest single-site subsample of the data. Our replication and extension of altered connectivity within and between the DMN, SN and CCN highlighted previously reported and new differences between patients with MDD and controls, and revealed features that might predict remission prior to pharmacotherapy.Trial registrationClinicalTrials.gov: NCT01655706.

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