{"id":{"repo_id":"cape-town","oai_identifier":"oai:open.uct.ac.za:11427/20957"},"canonical_url":"https://search.dev.ndltd.org/etd/cape-town/oai:open.uct.ac.za:11427/20957","repository":{"repo_id":"cape-town","name":"University of Cape Town","base_url":"https://open.uct.ac.za/oai/request"},"display":{"title":"Simultaneous DTI and rs-fMRI using the navigated diffusion sequence","abstract":"Blood oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) experiments are normally performed separately. The idea of extracting inherently co-registered activation/connectivity maps and diffusion parameters has resulted in efforts to develop methods for simultaneous fMRI and DTI data acquisition. Recently, a 3D echo planar imaging (EPI) acquisition was successfully inserted after each DTI volume to perform real-time motion correction, with the two sequence protocols remaining separate. We examined using a single 3D EPI acquisition, inserted following each DTI volume acquisition (hereafter called the single nav sequence), modified to acquire BOLD resting state fMRI (rs-fMRI) data. We also investigated inserting a second 3D EPI acquisition in the middle of each DTI volume acquisition (hereafter called the double nav sequence) to increase fMRI temporal resolution. Two adult subjects were scanned with the navigated sequences and the standard separate 2D EPI BOLD and DTI acquisitions for comparison. Preprocessing and analysis of data was performed using FATCAT, AFNI , FSL and in-house Python scripts. Four standard resting state networks (RSNs) were visually identified using the navigated diffusion sequences. While RSNs were apparent in the single nav case, they were quite noisy and in some cases entire regions did not show connectivity. The double nav connectivity maps were more similar to the standard BOLD connectivity maps in terms of the spatial extent of the regions showing connectivity to the seed. The whole brain distributions of fractional anisotropy (FA) and mean diffusivity (MD) were similar among the different acquisition protocols. The jackknife standard error was comparable between the navigated and standard protocols. Further comparisons of diffusion data made using probabilistic tractography and connectivity matrices showed overall small differences indicating that connections derived from the standard DTI, single nav and double nav protocols were overall similar. We have therefore shown a significant \"proof of concept\" of successfully acquiring simultaneous DTI and rs-fMRI data, and therefore for investigating brain structural and functional connectivity simultaneously.","abstract_html":"Blood oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) experiments are normally performed separately. The idea of extracting inherently co-registered activation/connectivity maps and diffusion parameters has resulted in efforts to develop methods for simultaneous fMRI and DTI data acquisition. Recently, a 3D echo planar imaging (EPI) acquisition was successfully inserted after each DTI volume to perform real-time motion correction, with the two sequence protocols remaining separate. We examined using a single 3D EPI acquisition, inserted following each DTI volume acquisition (hereafter called the single nav sequence), modified to acquire BOLD resting state fMRI (rs-fMRI) data. We also investigated inserting a second 3D EPI acquisition in the middle of each DTI volume acquisition (hereafter called the double nav sequence) to increase fMRI temporal resolution. Two adult subjects were scanned with the navigated sequences and the standard separate 2D EPI BOLD and DTI acquisitions for comparison. Preprocessing and analysis of data was performed using FATCAT, AFNI , FSL and in-house Python scripts. Four standard resting state networks (RSNs) were visually identified using the navigated diffusion sequences. While RSNs were apparent in the single nav case, they were quite noisy and in some cases entire regions did not show connectivity. The double nav connectivity maps were more similar to the standard BOLD connectivity maps in terms of the spatial extent of the regions showing connectivity to the seed. The whole brain distributions of fractional anisotropy (FA) and mean diffusivity (MD) were similar among the different acquisition protocols. The jackknife standard error was comparable between the navigated and standard protocols. Further comparisons of diffusion data made using probabilistic tractography and connectivity matrices showed overall small differences indicating that connections derived from the standard DTI, single nav and double nav protocols were overall similar. We have therefore shown a significant "proof of concept" of successfully acquiring simultaneous DTI and rs-fMRI data, and therefore for investigating brain structural and functional connectivity simultaneously.","abstract_has_math":false,"creators":["Mofya, Mwape"],"institution":"Division of Biomedical Engineering","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Meintjes, Ernesta M","Alhamud, Alkathafi Ali","Taylor, Paul A"],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016","date_published":"2016","updated_at":"2026-07-22T22:23:19Z","subjects":[],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/11427/20957","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Meintjes, Ernesta M","Alhamud, Alkathafi Ali","Taylor, Paul A"]},{"key":"dc:creator","label":"Author","values":["Mofya, Mwape"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2016-07-28T12:20:09Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2016-07-28T12:20:09Z"]},{"key":"dc:date.issued","label":"Date","values":["2016"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["Division of Biomedical Engineering"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of Cape Town"]},{"key":"dc:type","label":"Dc Type","values":["Master Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Masters"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["MSc (Med)"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/11427/20957"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Blood oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) experiments are normally performed separately. The idea of extracting inherently co-registered activation/connectivity maps and diffusion parameters has resulted in efforts to develop methods for simultaneous fMRI and DTI data acquisition. Recently, a 3D echo planar imaging (EPI) acquisition was successfully inserted after each DTI volume to perform real-time motion correction, with the two sequence protocols remaining separate. We examined using a single 3D EPI acquisition, inserted following each DTI volume acquisition (hereafter called the single nav sequence), modified to acquire BOLD resting state fMRI (rs-fMRI) data. We also investigated inserting a second 3D EPI acquisition in the middle of each DTI volume acquisition (hereafter called the double nav sequence) to increase fMRI temporal resolution. Two adult subjects were scanned with the navigated sequences and the standard separate 2D EPI BOLD and DTI acquisitions for comparison. Preprocessing and analysis of data was performed using FATCAT, AFNI , FSL and in-house Python scripts. Four standard resting state networks (RSNs) were visually identified using the navigated diffusion sequences. While RSNs were apparent in the single nav case, they were quite noisy and in some cases entire regions did not show connectivity. The double nav connectivity maps were more similar to the standard BOLD connectivity maps in terms of the spatial extent of the regions showing connectivity to the seed. The whole brain distributions of fractional anisotropy (FA) and mean diffusivity (MD) were similar among the different acquisition protocols. The jackknife standard error was comparable between the navigated and standard protocols. Further comparisons of diffusion data made using probabilistic tractography and connectivity matrices showed overall small differences indicating that connections derived from the standard DTI, single nav and double nav protocols were overall similar. We have therefore shown a significant \"proof of concept\" of successfully acquiring simultaneous DTI and rs-fMRI data, and therefore for investigating brain structural and functional connectivity simultaneously."]},{"key":"dc:title","label":"Title","values":["Simultaneous DTI and rs-fMRI using the navigated diffusion sequence"]}]}],"canonical_facts":{"dc:contributor.advisor":["Meintjes, Ernesta M","Alhamud, Alkathafi Ali","Taylor, Paul A"],"dc:creator":["Mofya, Mwape"],"dc:date.accessioned":["2016-07-28T12:20:09Z"],"dc:date.available":["2016-07-28T12:20:09Z"],"dc:date.issued":["2016"],"dc:description.abstract":["Blood oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) experiments are normally performed separately. The idea of extracting inherently co-registered activation/connectivity maps and diffusion parameters has resulted in efforts to develop methods for simultaneous fMRI and DTI data acquisition. Recently, a 3D echo planar imaging (EPI) acquisition was successfully inserted after each DTI volume to perform real-time motion correction, with the two sequence protocols remaining separate. We examined using a single 3D EPI acquisition, inserted following each DTI volume acquisition (hereafter called the single nav sequence), modified to acquire BOLD resting state fMRI (rs-fMRI) data. We also investigated inserting a second 3D EPI acquisition in the middle of each DTI volume acquisition (hereafter called the double nav sequence) to increase fMRI temporal resolution. Two adult subjects were scanned with the navigated sequences and the standard separate 2D EPI BOLD and DTI acquisitions for comparison. Preprocessing and analysis of data was performed using FATCAT, AFNI , FSL and in-house Python scripts. Four standard resting state networks (RSNs) were visually identified using the navigated diffusion sequences. While RSNs were apparent in the single nav case, they were quite noisy and in some cases entire regions did not show connectivity. The double nav connectivity maps were more similar to the standard BOLD connectivity maps in terms of the spatial extent of the regions showing connectivity to the seed. The whole brain distributions of fractional anisotropy (FA) and mean diffusivity (MD) were similar among the different acquisition protocols. The jackknife standard error was comparable between the navigated and standard protocols. Further comparisons of diffusion data made using probabilistic tractography and connectivity matrices showed overall small differences indicating that connections derived from the standard DTI, single nav and double nav protocols were overall similar. We have therefore shown a significant \"proof of concept\" of successfully acquiring simultaneous DTI and rs-fMRI data, and therefore for investigating brain structural and functional connectivity simultaneously."],"dc:identifier.uri":["http://hdl.handle.net/11427/20957"],"dc:language.iso":["eng"],"dc:publisher.department":["Division of Biomedical Engineering"],"dc:publisher.institution":["University of Cape Town"],"dc:title":["Simultaneous DTI and rs-fMRI using the navigated diffusion sequence"],"dc:type":["Master Thesis"],"dc:type.qualificationlevel":["Masters"],"dc:type.qualificationname":["MSc (Med)"]},"updated_at":"2026-07-22T22:23:19Z"}