Improved respiratory efficiency of 3D late gadolinium enhancement imaging using the continuously adaptive windowing strategy (CLAWS).

PURPOSE: Acquisition durations of navigator-gated high-resolution three-dimensional late gadolinium enhancement studies may typically be up to 10 min, depending on the respiratory efficiency and heart rate. Implementation of the continuously adaptive windowing strategy (CLAWS) could increase respiratory efficiency, but the resulting non-smooth k-space acquisition order during gadolinium wash-out could result in increased artifact. METHODS: Navigator-gated three-dimensional late gadolinium enhancement acquisitions were performed in 18 patients using tracking end-expiratory accept/reject (EE-ARA) and CLAWS algorithms in random order. RESULTS: Retrospective analysis of the stored navigator data shows that CLAWS scan times are very close to (within 1%) or equal to the fastest achievable scan times while EE-ARA significantly extends the acquisition duration (P < 0.0001). EE-ARA acquisitions are 26% longer than CLAWS acquisitions (378 ± 104 s compared to 301 ± 85 s, P = 0.002). Image quality scores for CLAWS and EE-ARA acquisitions are not significantly different (4.1 ± 0.6 compared to 4.3 ± 0.6, P = ns). Numerical phantom simulations show that the non-uniform k-space ordering introduced by CLAWS results in slight, but not statistically significant, reductions in both blood signal-to-noise ratio (10%) and blood-myocardium contrast-to-noise ratio (12%). CONCLUSIONS: CLAWS results in markedly reduced acquisition durations compared to EE-ARA without significant detriment to the image quality.

Dynamic reconfiguration of human brain functional networks through neurofeedback.

Recent fMRI studies demonstrated that functional connectivity is altered following cognitive tasks (e.g., learning) or due to various neurological disorders. We tested whether real-time fMRI-based neurofeedback can be a tool to voluntarily reconfigure brain network interactions. To disentangle learning-related from regulation-related effects, we first trained participants to voluntarily regulate activity in the auditory cortex (training phase) and subsequently asked participants to exert learned voluntary self-regulation in the absence of feedback (transfer phase without learning). Using independent component analysis (ICA), we found network reconfigurations (increases in functional network connectivity) during the neurofeedback training phase between the auditory target region and (1) the auditory pathway; (2) visual regions related to visual feedback processing; (3) insula related to introspection and self-regulation and (4) working memory and high-level visual attention areas related to cognitive effort. Interestingly, the auditory target region was identified as the hub of the reconfigured functional networks without a-priori assumptions. During the transfer phase, we again found specific functional connectivity reconfiguration between auditory and attention network confirming the specific effect of self-regulation on functional connectivity. Functional connectivity to working memory related networks was no longer altered consistent with the absent demand on working memory. We demonstrate that neurofeedback learning is mediated by widespread changes in functional connectivity. In contrast, applying learned self-regulation involves more limited and specific network changes in an auditory setup intended as a model for tinnitus. Hence, neurofeedback training might be used to promote recovery from neurological disorders that are linked to abnormal patterns of brain connectivity.

Recovery of the default mode network after demanding neurofeedback training occurs in spatio-temporally segregated subnetworks.

The default mode (DM) network is a major large-scale cerebral network that can be identified with functional magnetic resonance imaging (fMRI) during resting state. Most studies consider functional connectivity networks as stationary phenomena. Consequently, the transient behavior of the DM network and its subnetworks is still largely unexplored. Most functional connectivity fMRI studies assess the steady state of resting without any task. To specifically investigate the recovery of the DM network during the transition from activation to rest, we implemented a cognitively demanding real-time fMRI neurofeedback task that targeted down-regulation of the primary auditory cortex. Each of twelve healthy subjects performed 16 block-design fMRI runs (4 runs per day repeated on 4 days) resulting 192 runs in total. The analysis included data-driven independent component analysis (ICA) and high-resolution latency estimation between the four components that corresponded to subnetworks of the DM network. These different subnetworks reemerged after regulation with an average time lag or 3.3s and a time lag of 4.4s between the first and fourth components; i.e., the DM recovery first shifts from anterior to posterior, and then gradually focuses on the ventral part of the posterior cingulate cortex, which is known to be implicated in internally directed cognition. In addition, we found less reactivation in the early anterior subnetwork as regulation strength increased, but more reactivation with larger regulation for the late subnetwork that encompassed the ventral PCC. This finding confirms that the level of task engagement influences inversely the subsequent recovery of regions related to attention compared to those related to internally directed cognition.

MR-imaging of the thoracic aorta: 3D-ECG- and respiratory-gated bSSFP imaging using the CLAWS algorithm versus contrast-enhanced 3D-MRA.

OBJECTIVE: To compare a contrast-enhanced 3D angiography (CE-3D-MRA) with the ECG- and respiratory gated 3D balanced steady state free precession (bSSFP) sequence using the CLAWS algorithm (3D-bSSFP-CLAWS) with respect to acquisition time, image quality, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). METHODS: 14 patients (4 women, mean age ± SD: 52 ± 18) with known or suspected thoracic aortic disease were imaged on a 1.5T scanner with both sequences. Two readers scored image quality of predefined levels of the thoracic aorta. Acquisition time, SNR and CNR were calculated for each examination. RESULTS: Image quality achieved with the 3D-bSSFP-CLAWS was scored significantly better than with the CE-3D-MRA for the aortic annulus (P = 0.003), the sinuses of Valsalva (P = 0.001), the proximal coronary arteries (P = 0.001) and the sinotubular junction (P = 0.001). Effective acquisition time for the 3D-bSSFP-CLAWS and corrected acquisition time (corrected for imaging parameters) was significantly longer compared to the CE-3D-MRA (P = 0.004 and P = 0.028). SNR and CNR were significantly higher for the CE-3D-MRA (P = 0.007 and P = 0.001). CONCLUSIONS: Providing the highest scan efficiency for a given breathing pattern, image quality for the proximal ascending aorta achieved with the 3D-bSSFP-CLAWS is significantly superior in contrast to the CE-3D-MRA.