Neural basis and behavioral effects of dynamic resting state functional magnetic resonance imaging as defined by sliding window correlation and quasi-periodic patterns

fMRI;Dynamic analysis;Sliding window;Sliding windows;Resting state;Default mode;Functional connectivity;Functional network;Correlation;Coherence;Functional magnetic resonance imaging;Local field potentials;LFP;Band-limited power;BLP;Inter-individual;Intra-individual;Spontaneous activity;Brain;Primary somatosensory cortex;Rodent;Rat;Human;Simultaneous experiments;Neural basis;Glial basis;Neurons;Astrocytes;Vasomotion;Hemodynamic response;Dynamic;Spatiotemporal dynamics;Quasi-periodic patterns;Brain networks;Resting state networks;Anesthesia;Dexmedetomidine;Isoflurane;Awake;Global signal

Thompson, Garth John ; Keilholz, Shella D. Biomedical Engineering (Joint GT/Emory Department) Hu, Xiaoping Jaeger, Dieter Stanley, Garrett B. Epstein, Charles M. ; Keilholz, Shella D.

University:Georgia Institute of Technology

Department:Biomedical Engineering (Joint GT/Emory Department)

关键词: fMRI;    Dynamic analysis;    Sliding window;    Sliding windows;    Resting state;    Default mode;    Functional connectivity;    Functional network;    Correlation;    Coherence;    Functional magnetic resonance imaging;    Local field potentials;    LFP;    Band-limited power;    BLP;    Inter-individual;    Intra-individual;    Spontaneous activity;    Brain;    Primary somatosensory cortex;    Rodent;    Rat;    Human;    Simultaneous experiments;    Neural basis;    Glial basis;    Neurons;    Astrocytes;    Vasomotion;    Hemodynamic response;    Dynamic;    Spatiotemporal dynamics;    Quasi-periodic patterns;    Brain networks;    Resting state networks;    Anesthesia;    Dexmedetomidine;    Isoflurane;    Awake;    Global signal;   

Others  :  https://smartech.gatech.edu/bitstream/1853/49083/1/THOMPSON-DISSERTATION-2013.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

While task-based functional magnetic resonance imaging (fMRI) has helped us understand the functional role of many regions in the human brain, many diseases and complex behaviors defy explanation. Alternatively, if no task is performed, the fMRI signal between distant, anatomically connected, brain regions is similar over time. These correlations in “resting state” fMRI have been strongly linked to behavior and disease. Previous work primarily calculated correlation in entire fMRI runs of six minutes or more, making understanding the neural underpinnings of these fluctuations difficult. Recently, coordinated dynamic activity on shorter time scales has been observed in resting state fMRI: correlation calculated in comparatively short sliding windows and quasi-periodic (periodic but not constantly active) spatiotemporal patterns. However, little relevance to behavior or underlying neural activity has been demonstrated. This dissertation addresses this problem, first by using 12.3 second windows to demonstrate a behavior-fMRI relationship previously only observed in entire fMRI runs. Second, simultaneous recording of fMRI and electrical signals from the brains of anesthetized rats is used to demonstrate that both types of dynamic activity have strong correlates in electrophysiology. Very slow neural signals correspond to the quasi-periodic patterns, supporting the idea that low-frequency activity organizes large scale information transfer in the brain. This work both validates the use of dynamic analysis of resting state fMRI, and provides a starting point for the investigation of the systemic basis of many neuropsychiatric diseases.