Regional coherence changes in the early stages of Alzheimer's disease: A combined structural and resting-state functional MRI study

By Yong He, Liang Wang, Yufeng Zang, Lixia Tian, Xinqing Zhang, Kuncheng Li, T. Jiang. NeuroImage, 2007

550 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/a7559045b891e3e4c437a1a988caa2a5d59478b7

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In Cognitive Neuroscience

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

A review on neuroimaging‐based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages (2017, unknown): “AD compromises primary brain targets, such as the DMN, by disrupting their functional activity (He et al., 2007; Li et al., 2002), as well as the functional connectivity between primary targets and the remaining parts of the brain (Wang et al., 2007a, 2006).”
How Visual Is the Visual Cortex? Comparing Connectional and Functional Fingerprints between Congenitally Blind and Sighted Individuals (2015, methodology): “For the potential influence of gray matter density on RSFC patterns (He et al., 2007; Wang et al., 2011), we calculated the partial correlation between the between-group similarity of RSFC patterns and the between-group similarity of category response patterns by including the between-group…”
Uncovering Intrinsic Modular Organization of Spontaneous Brain Activity in Humans (2009, background): “spontaneous BOLD signals, such as regional neuronal coherence [4,5] and fractal complexity [6].”
Loss of ‘Small-World’ Networks in Alzheimer's Disease: Graph Analysis of fMRI Resting-State Functional Connectivity (2010, methodology): “The present data set was analyzed with tensor PICA, also demonstrating a decrease in activity in AD patients compared with healthy subjects within the dorsal visual-spatial attention system and DMN [7,8,55,58,59,60].”
Resting State fMRI: A Personal History (2012, methodology): “As an extension, in addition to temporal correlation, coherence analysis has also been performed to investigate similarity across frequency spectrum (He et al., 2007).”
DPARSF: A MATLAB Toolbox for “Pipeline” Data Analysis of Resting-State fMRI (2010, methodology): “The power spectrum of the LFF (equivalent to the square of the ALFF) has been used to indicate the magnitude of neural activity (Kiviniemi et al., 2000; He et al., 2007; Hoptman et al., 2010; Zhang et al., in press).”
REST: A Toolkit for Resting-State Functional Magnetic Resonance Imaging Data Processing (2011, methodology): “ReHo and ALFF have been used to study brain disorders including attention deficit hyperactivity disorder (ADHD) [14,15], schizophrenia [16–18], depression [19,20], Alzheimer’s disease (AD) [21–23], mild cognitive impairment [24], Parkinson disease (PD) [25], epilepsy [26], and posttraumatic stress d”
An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: fractional ALFF. (2008, unknown): “Combining electroneurophysiological recordings and fMRI, many studies have suggested that the LFFs of blood oxygenation level-dependent (BOLD) fMRI signals are closely related to the spontaneous neuronal activities (Goldman et al., 2002; Logothetis et al., 2001; Lu et al., 2007; Mantini et al., 2007”
Frequency-dependent changes in the amplitude of low-frequency fluctuations in amnestic mild cognitive impairment: A resting-state fMRI study (2011, background): “Several groups have also showed abnormal LFO amplitudes in brain diseases such as the attention-deficit/hyperactivity disorder (Zang et al., 2007), AD (He et al., 2007), schizophrenia (Hoptman et al., 2010), and mesial temporal lobe epilepsy (Zhang et al., 2008).”
Structural Insights into Aberrant Topological Patterns of Large-Scale Cortical Networks in Alzheimer's Disease (2008, methodology): “In our previous study, using graph theoretical network analysis (GRETNA), we demonstrated that coordinated variations in the thickness of the human cerebral cortex at a macroscale (regional level) is neither regular nor random but “small world” in nature (He et al., 2007b).”
Default-mode brain dysfunction in mental disorders: a systematic review. (2009, methodology): “…disease is one of the most widely investigated pathologies, particularly in view of the putative association between the DMN and working memory (Buckner et al., 2005; Firbank et al., 2007; Greicius et al., 2004; He et al., 2007; Rombouts et al., 2005; Sorg et al., 2007; Wang et al., 2006).”
Toward reliable characterization of functional homogeneity in the human brain: Preprocessing, scan duration, imaging resolution and computational space (2012, unknown): “disease (He et al., 2007; Liu et al., 2008; Zhang et al., 2012), epilepsy (Zhong et al.”
Deep Learning Approach for Early Detection of Alzheimer’s Disease (2021, background): “So , the early detection of AD at its prodromal stage is critical [10, 11].”
Reduced resting-state brain activity in the "default network" in normal aging. (2008, methodology): “Nevertheless, because decreased brain activity may be (partly) explained by decreased GM volume, as shown previously (Johnson et al. 2000; Prvulovic et al. 2002; He et al. 2007), it is important to control for GM volumes.”
DeepAD: Alzheimer’s Disease Classification via Deep Convolutional Neural Networks using MRI and fMRI (2016, background): “Diagnosing Alzheimer’s disease requires very careful medical assessment, including patient history, a mini mental state examination (MMSE), and physical and neurobiological exams [1] [2].”