{"@context":"http://iiif.io/api/presentation/2/context.json","@id":"https://repo.library.stonybrook.edu/cantaloupe/iiif/2/manifest.json","@type":"sc:Manifest","label":"Temporal fidelity and functional dynamics in functional MRI time-series analyses","metadata":[{"label":"dc.description.sponsorship","value":"This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree."},{"label":"dc.format","value":"Monograph"},{"label":"dc.format.medium","value":"Electronic Resource"},{"label":"dc.identifier.uri","value":"http://hdl.handle.net/11401/76981"},{"label":"dc.language.iso","value":"en_US"},{"label":"dc.publisher","value":"The Graduate School, Stony Brook University: Stony Brook, NY."},{"label":"dcterms.abstract","value":"Functional magnetic resonance imaging (fMRI) has revolutionized the study of the human brain by allowing researchers and clinicians to map neural function at fine spatial resolutions. Modern fMRI acquisition and analytic techniques are beginning to uncover the network-scale dynamics of meso-circuits within the brain, which could lead to enhanced treatments or prevention of psychiatric and neurological illnesses. However, artifact and noise potentially compromise the validity of dynamic analyses. Here, we evaluate and utilize dynamic fMRI signals to advance the study of the human brain. The dissertation comprises three parts: a human study, a phantom study, and a combined human-phantom study. First, we use novel analyses and modeling techniques to define a brain meso-circuit associated with threat evaluation. We show that the inferior frontal gyrus (IFG) is significantly dysregulated in anxiety patients, and use causal modeling to reveal aberrant circuitry between the IFG, ventromedial prefrontal cortex, and amygdala during processing of ambiguous threat. Second, we prototyped (designed, manufactured, and validated) a dynamic phantom that enables the rigorous evaluation of the temporal fidelity of fMRI time-series with respect to controlled dynamic inputs. The phantom comprises concentric cylinders containing agarose gels whose magnetic susceptibilities are spatially varying; a novel pneumatic motor and fiber optic feedback system are coupled with a microcontroller to produce precisely timed changes in fMRI signal within predefined regions of the phantom. We validate the phantom by demonstrating biomimetic response functions and motion artifact-free output fMRI time-series. Third, we combine human and phantom data from three separate fMRI scanners to aid in the development of a novel measure of signal to noise for resting-state studies, deemed signal fluctuation sensitivity (SFS). We first use the phantom to develop and test SFS on data with known inputs, and compare SFS with classical temporal SNR (tSNR). We then validate SFS using human resting-state data, and demonstrate advantages over tSNR. Taken together, the three elements of this dissertation advance fMRI state of the art, with broad implications for clinical neuroimaging."},{"label":"dcterms.available","value":"2017-09-20T16:51:35Z"},{"label":"dcterms.contributor","value":"Button, Terry."},{"label":"dcterms.creator","value":"DeDora, Dan James"},{"label":"dcterms.dateAccepted","value":"2017-09-20T16:51:35Z"},{"label":"dcterms.dateSubmitted","value":"2017-09-20T16:51:35Z"},{"label":"dcterms.description","value":"Department of Biomedical Engineering."},{"label":"dcterms.extent","value":"88 pg."},{"label":"dcterms.format","value":"Monograph"},{"label":"dcterms.identifier","value":"http://hdl.handle.net/11401/76981"},{"label":"dcterms.issued","value":"2015-05-01"},{"label":"dcterms.language","value":"en_US"},{"label":"dcterms.provenance","value":"Made available in DSpace on 2017-09-20T16:51:35Z (GMT). No. of bitstreams: 1\nDeDora_grad.sunysb_0771E_12274.pdf: 11190077 bytes, checksum: 39ed0a969837c977708d6e13eb2615f0 (MD5)\n  Previous issue date: 2015"},{"label":"dcterms.publisher","value":"The Graduate School, Stony Brook University: Stony Brook, NY."},{"label":"dcterms.subject","value":"Biomedical engineering"},{"label":"dcterms.title","value":"Temporal fidelity and functional dynamics in functional MRI time-series analyses"},{"label":"dcterms.type","value":"Dissertation"},{"label":"dc.type","value":"Dissertation"}],"description":"This manifest was generated dynamically","viewingDirection":"left-to-right","sequences":[{"@type":"sc:Sequence","canvases":[{"@id":"https://repo.library.stonybrook.edu/cantaloupe/iiif/2/canvas/page-1.json","@type":"sc:Canvas","label":"Page 1","height":1650,"width":1275,"images":[{"@type":"oa:Annotation","motivation":"sc:painting","resource":{"@id":"https://repo.library.stonybrook.edu/cantaloupe/iiif/2/13%2F13%2F58%2F1313587282276224390992899777458393959/full/full/0/default.jpg","@type":"dctypes:Image","format":"image/jpeg","height":1650,"width":1275,"service":{"@context":"http://iiif.io/api/image/2/context.json","@id":"https://repo.library.stonybrook.edu/cantaloupe/iiif/2/13%2F13%2F58%2F1313587282276224390992899777458393959","profile":"http://iiif.io/api/image/2/level2.json"}},"on":"https://repo.library.stonybrook.edu/cantaloupe/iiif/2/canvas/page-1.json"}]}]}]}