A Multimodal Structural and Functional Neuroimaging Study of Amnestic Mild Cognitive Impairment

Bharath, Srikala; Joshi, Himanshu; John, John P.; Balachandar, Rakesh; Sadanand, Shilpa; Saini, Jitendra; Kumar, Keshav; Varghese, Mathew

doi:10.1016/j.jagp.2016.05.001

Cited by 36 publications

(13 citation statements)

References 52 publications

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“…Resting-state functional magnetic resonance imaging (rfMRI) has been increasingly used as a reliable method (Zuo and Xing, 2014) to detect brain network abnormalities in aMCI (Agosta et al, 2012; Bharath et al, 2017; Wang et al, 2018b) or AD (Binnewijzend et al, 2014; Wang et al, 2018b). The rfMRI findings in AD and MCI are rather consistent across different studies in different networks, such as default mode network (Agosta et al, 2012), somatomotor network (Albers et al, 2015), dorsal attention network (Qian et al, 2015), limbic network (Nestor et al, 2003), and frontoparietal control network (Agosta et al, 2012; Brier et al, 2012; Munro et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Functional Connectivity Changes Across the Spectrum of Subjective Cognitive Decline, Amnestic Mild Cognitive Impairment and Alzheimer’s Disease

Wang

Qiao

Chen

et al. 2019

Front. Neuroinform.

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The abnormality occurs at molecular, cellular as well as network levels in patients with Alzheimer’s disease (AD) prior to diagnosis. Most previous connectivity studies were conducted at 1 out of 3 (local, meso and global) scales in subjects covering only part of the entire AD spectrum (subjective cognitive decline, SCD; amnestic mild cognitive impairment, aMCI; and then fully manifest AD). Data interpretation within the framework of disease progression is therefore difficult. The current study included 3 age- and sex-matched cohorts: SCD ( n = 32), aMCI ( n = 37) and fully-established AD ( n = 30). A group of healthy elderly subjects ( n = 40) were included as a normal control (NC). Network connectivity was examined at the local (degree centrality), meso [subgraph centrality (SC)], and global (eigenvector and page-rank centralities) levels. As compared to NC, SCD subjects had isolated decrease of SC in primary (somatomotor and visual) networks. aMCI subjects had decreased centralities at all three scales in associative (frontoparietal control, dorsal attention, limbic and default) networks. AD subjects had increased centrality at the global scale in all seven networks. There was a positive association between Montreal Cognitive Assessment (MoCA) scores and DC in the frontoparietal control network in SCD, a negative relationship between Mini-Mental State Examination (MMSE) scores and EC in the somatomotor network in AD. These findings suggest that the primary network is impaired as early as in SCD. Impairment in the associative network also starts at the local level at this stage and may contribute to the cognitive decline. As associative network impairment extends from local to meso and global scales in aMCI, compensatory mechanisms in the primary network are activated.

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Section: Introductionmentioning

confidence: 99%

Functional Connectivity Changes Across the Spectrum of Subjective Cognitive Decline, Amnestic Mild Cognitive Impairment and Alzheimer’s Disease

Wang

Qiao

Chen

et al. 2019

Front. Neuroinform.

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“…TBSS increases the sensitivity and reliability of analysis of multi-subject diffusion imaging ( 14 ). It has been utilized for detecting alterations in subjects with movement or neurodegenerative disorders, such as Alzheimer’s dementia, Parkinson’s disease, and mild cognitive impairment ( 15 – 17 ).…”

Section: Introductionmentioning

confidence: 99%

Microstructural Alterations in Asymptomatic and Symptomatic Patients with Spinocerebellar Ataxia Type 3: A Tract-Based Spatial Statistics Study

Liao

Zhan

et al. 2017

Front. Neurol.

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ObjectiveSpinocerebellar ataxia type 3 (SCA3) is the most commonly occurring type of autosomal dominant spinocerebellar ataxia. The present study aims to investigate progressive changes in white matter (WM) fiber in asymptomatic and symptomatic patients with SCA3.MethodsA total of 62 participants were included in this study. Among them, 16 were asymptomatic mutation carriers (pre-SCA3), 22 were SCA3 patients with clinical symptoms, and 24 were normal controls (NC). Group comparison of tract-based spatial statistics was performed to identify microstructural abnormalities at different SCA3 disease stages.ResultsDecreased fractional anisotropy (FA) and increased mean diffusivity (MD) were found in the left inferior cerebellar peduncle and superior cerebellar peduncle (SCP) in the pre-SCA3 group compared with NC. The symptomatic SCA3 group showed brain-wide WM tracts impairment in both supratentorial and infratentorial networks, and the mean FA value of the WM skeleton showed a significantly negative correlation with the International Cooperative Ataxia Rating Scale (ICARS) scores. Specifically, FA of the bilateral posterior limb of the internal capsule negatively correlated with SCA3 disease duration. We also found that FA values in the right medial lemniscus and SCP negatively correlated with ICARS scores, whereas FA in the right posterior thalamic radiation positively correlated with Montreal Cognitive Assessment scores. In addition, MD in the middle cerebellar peduncle, left anterior limb of internal capsule, external capsule, and superior corona radiate positively correlated with ICARS scores in SCA3 patients.ConclusionWM microstructural changes are present even in the asymptomatic stages of SCA3. In individuals in which the disease has progressed to the symptomatic stage, the integrity of WM fibers across the whole brain is affected. Furthermore, abnormalities in WM tracts are closely related to SCA3 disease severity, including movement disorder and cognitive dysfunction. These findings can deepen our understanding of the neural basis of SCA3 dysfunction.

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“…Therefore, in the future work, it is worth extending the hypothesis testing to networks with self-loops and directed structures. Actually, in this work, networks including selfloops can be tackled by removing the assumption A 2,ii = 0 and modifying the scale factor n − 1 to n in the statistic (2) and Theorem 3 would hold for this case. For directed network, maybe we need to build a new test under this framework.…”

Section: Discussionmentioning

confidence: 99%

A Hypothesis Testing for Large Weighted Networks With Applications to Functional Neuroimaging Data

2020

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Neuroimaging techniques have been routinely applied in various studies in neuroscience, which contribute to providing novel insights into brain functions. One of the most important and challenging questions related to data collected from such studies is hypothesis testing for the differences between two samples of networks of brain regions. This is due to the fact that networks constructed from neuroimaging studies, which can be weighted and large, are very complex. Focusing on this problem, a novel hypothesis testing procedure is proposed under a general framework for large weighted networks. The asymptotic null distribution is derived and the power guarantee is also provided theoretically. Simulation experiments and practical application to the real brain networks are carried out to demonstrate the effectiveness of the proposed testing method.

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A Multimodal Structural and Functional Neuroimaging Study of Amnestic Mild Cognitive Impairment

Cited by 36 publications

References 52 publications

Functional Connectivity Changes Across the Spectrum of Subjective Cognitive Decline, Amnestic Mild Cognitive Impairment and Alzheimer’s Disease

Functional Connectivity Changes Across the Spectrum of Subjective Cognitive Decline, Amnestic Mild Cognitive Impairment and Alzheimer’s Disease

Microstructural Alterations in Asymptomatic and Symptomatic Patients with Spinocerebellar Ataxia Type 3: A Tract-Based Spatial Statistics Study

A Hypothesis Testing for Large Weighted Networks With Applications to Functional Neuroimaging Data

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