Global ETD Search

1	Quasi-Periodic Patterns of Neural Activity improve Classification of Alzheimer’s Disease in Mice Belloy, Michaël E., Shah, Disha, Abbas, Anzar, Kashyap, Amrit, Roßner, Steffen, Van der Linden, Annemie, Keilholz, Shella D., Keliris, Georgios A., Verhoye, Marleen 08 November 2024 (has links) Resting state (rs)fMRI allows measurement of brain functional connectivity and has identified default mode (DMN) and task positive (TPN) network disruptions as promising biomarkers for Alzheimer’s disease (AD). Quasi-periodic patterns (QPPs) of neural activity describe recurring spatiotemporal patterns that display DMN with TPN anti-correlation. We reasoned that QPPs could provide new insights into AD network dysfunction and improve disease diagnosis. We therefore used rsfMRI to investigate QPPs in old TG2576 mice, a model of amyloidosis, and age-matched controls. Multiple QPPs were determined and compared across groups. Using linear regression, we removed their contribution from the functional scans and assessed how they reflected functional connectivity. Lastly, we used elastic net regression to determine if QPPs improved disease classification. We present three prominent findings: (1) Compared to controls, TG2576 mice were marked by opposing neural dynamics in which DMN areas were anti-correlated and displayed diminished anti-correlation with the TPN. (2) QPPs reflected lowered DMN functional connectivity in TG2576 mice and revealed significantly decreased DMN-TPN anti-correlations. (3) QPP-derived measures significantly improved classification compared to conventional functional connectivity measures. Altogether, our findings provide insight into the neural dynamics of aberrant network connectivity in AD and indicate that QPPs might serve as a translational diagnostic tool. info:eu-repo/classification/ddc/610 ddc:610
2	Neural basis and behavioral effects of dynamic resting state functional magnetic resonance imaging as defined by sliding window correlation and quasi-periodic patterns Thompson, Garth John 20 September 2013 (has links) 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. 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 Magnetic resonance imaging Brain Pathophysiology Brain mapping

Search results

Quasi-Periodic Patterns of Neural Activity improve Classification of Alzheimer’s Disease in Mice

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