fMRI Magnitude Research Articles

Late fMRI Response Components Are Altered in Autism Spectrum Disorder.

Disrupted cortical neural inhibition has been hypothesized to be a primary contributor to the pathophysiology of autism spectrum disorder (ASD). This hypothesis predicts that ASD will be associated with an increase in neural responses. We tested this prediction by comparing fMRI response magnitudes to simultaneous visual, auditory, and motor stimulation in ASD and neurotypical (NT) individuals. No increases in the initial transient response in any brain region were observed in ASD, suggesting that there is no increase in overall cortical neural excitability. Most notably, there were widespread fMRI magnitude increases in the ASD response following stimulation offset, approximately 6–8 s after the termination of sensory and motor stimulation. In some regions, the higher fMRI offset response in ASD could be attributed to a lack of an “undershoot”—an often observed feature of fMRI responses believed to reflect inhibitory processing. Offset response magnitude was associated with reaction times (RT) in the NT group and may explain an overall reduced RT in the ASD group. Overall, our results suggest that increases in neural responsiveness are present in ASD but are confined to specific components of the neural response, are particularly strong following stimulation offset, and are linked to differences in RT.

Phase fMRI Reveals More Sparseness and Balance of Rest Brain Functional Connectivity Than Magnitude fMRI.

Conventionally, brain function is inferred from the magnitude data of the complex-valued fMRI output. Since the fMRI phase image (unwrapped) provides a representation of brain internal magnetic fieldmap (by a constant scale difference), it can also be used to study brain function while providing a more direct representation of the brain's magnetic state. In this study, we collected a cohort of resting-state fMRI magnitude and phase data pairs from 600 subjects (age from 10 to 76, 346 males), decomposed the phase data by group independent component analysis (pICA), calculated the functional network connectivity (pFNC). In comparison with the magnitude-based brain function analysis (mICA and mFNC), we find that the pFNC matrix contains fewer significant functional connections (with p-value thresholding) than the mFNC matrix, which are sparsely distributed across the whole brain with near/far interconnections and positive/negative correlations in rough balance. We also find a few of brain rest sub-networks within the phase data, primarily in subcortical, cerebellar, and visual regions. Overall, our findings offer new insights into brain function connectivity in the context of a focus on the brain's internal magnetic state.

Phase fMRI informs whole-brain function connectivity balance across lifespan with connection-specific aging effects during the resting state.

A functional magnetic resonance imaging (fMRI) experiment produces complex-valued images consisting of pairwise magnitude and phase images. As different perspective on the same magnetic source, fMRI magnitude and phase data are complementary for brain function analysis. We collected 600-subject fMRI data during rest, decomposed via group-level independent component analysis (ICA) (mICA and pICA for magnitude and phase respectively), and calculated brain functional network connectivity matrices (mFC and pFC). The pFC matrix shows a fewer of significant connections balancedacross positive and negative relationships. In comparison, the mFC matrix contains a positively-biased pattern with more significant connections. Our experiment data analyses also show that human brain maintains a whole-brain connection balance in resting state across an age span from 10 to 76years, however, phase and magnitude data analyses reveal different connection-specific age effects on significant positive and negative subnetwork couplings.

Task-evoked brain functional magnetic susceptibility mapping by independent component analysis (ϿICA)

BackgroundConventionally, independent component analysis (ICA) is performed on an fMRI magnitude dataset to analyze brain functional mapping (AICA). By solving the inverse problem of fMRI, we can reconstruct the brain magnetic susceptibility (Ͽ) functional states. Upon the reconstructed Ͽ dataspace, we propose an ICA-based brain functional Ͽ mapping method (ϿICA) to extract task-evoked brain functional map. New methodsA complex division algorithm is applied to a timeseries of fMRI phase images to extract temporal phase changes (relative to an OFF-state snapshot). A computed inverse MRI (CIMRI) model is used to reconstruct a 4D brain Ͽ response dataset. ϿICA is implemented by applying a spatial InfoMax ICA algorithm to the reconstructed 4D Ͽ dataspace. ResultsWith finger-tapping experiments on a 7T system, the ϿICA-extracted Ͽ-depicted functional map is similar to the SPM-inferred functional Ͽ map by a spatial correlation of 0.67±0.05. In comparison, the AICA-extracted magnitude-depicted map is correlated with the SPM magnitude map by 0.81±0.05. The understanding of the inferiority of ϿICA to AICA for task-evoked functional map is an ongoing research topic. Comparison with existing methodsFor task-evoked brain functional mapping, we compare the data-driven ICA method with the task-correlated SPM method. In particular, we compare ϿICA with AICA for extracting task-correlated timecourses and functional maps. ConclusionϿICA can extract a Ͽ-depicted task-evoked brain functional map from a reconstructed Ͽ dataspace without the knowledge about brain hemodynamic responses. The ϿICA-extracted brain functional Ͽ map reveals a bidirectional BOLD response pattern that is unavailable (or different) from AICA.

Two pitfalls of BOLD fMRI magnitude-based neuroimage analysis: Non-negativity and edge effect

BOLD fMRI is accepted as a noninvasive imaging modality for neuroimaging and brain mapping. A BOLD fMRI dataset consists of magnitude and phase components. Currently, only the magnitude is used for neuroimage analysis. In this paper, we show that the fMRI-magnitude-based neuroimage analysis may suffer two pitfalls: one is that the magnitude is non-negative and cannot differentiate positive from negative BOLD activity; the other is an edge effect that may manifest as an edge enhancement or a spatial interior dip artifact at a local uniform BOLD region. We demonstrate these pitfalls via numeric simulations using a BOLD fMRI model and also via a phantom experiment. We also propose a solution by making use of the fMRI phase image, the counterpart of the fMRI magnitude.

Changes in fMRI magnitude data and phase data observed in block-design and event-related tasks

Functional magnetic resonance imaging (fMRI) data are acquired as a complex image pair including magnitude and phase information. The vast majority of fMRI experiments do not attempt to take advantage of the time varying phase information. The phase of the MRI signal is related to the local magnetic field changes, suggesting it may contain useful information about the source of hemodynamic activity. Analysis of phase data acquired from different fMRI experiments has shown the presence of activity in response to various stimuli. However, there have been no studies that have examined phase data in a larger group of subjects for multiple types of fMRI tasks nor have studies examined phase changes due to event-related stimuli. In this paper, we evaluate the correspondence between the magnitude and phase changes at a group level in a block-design motor tapping task and in an event-related auditory oddball task. The results for both block-design and event-related tasks indicate the presence of task-related information in the phase data with phase-only and magnitude-only approaches showing signal changes in the expected brain regions. Although there is more overall activity detected with magnitude data, the phase-only analysis also reveals activity in regions expected to be involved in the task, some of which were not significantly activated in the magnitude-only analysis, suggesting that the phase might provide some unique information. In addition, the phase can potentially increase sensitivity within regions also showing magnitude changes. Future work should focus on additional methods for combining the magnitude and phase data.

Modeling both the magnitude and phase of complex-valued fMRI data

In MRI and fMRI, images or voxel measurement are complex valued or bivariate at each time point. Recently, (Rowe, D.B., Logan, B.R., 2004. A complex way to compute fMRI activation. NeuroImage 23 (3), 1078–1092) introduced an fMRI magnitude activation model that utilized both the real and imaginary data in each voxel. This model, following traditional beliefs, specified that the phase time course were fixed unknown quantities which may be estimated voxel-by-voxel. Subsequently, (Rowe, D.B., Logan, B.R., 2005. Complex fMRI analysis with unrestricted phase is equivalent to a magnitude-only model. NeuroImage 24 (2), 603–606) generalized the model to have no restrictions on the phase time course. They showed that this unrestricted phase model was mathematically equivalent to the usual magnitude-only data model including regression coefficients and voxel activation statistic but philosophically different due to it derivation from complex data. Recent findings by (Hoogenrad, F.G., Reichenbach, J.R., Haacke, E.M., Lai, S., Kuppusamy, K., Sprenger, M., 1998. In vivo measurement of changes in venous blood-oxygenation with high resolution functional MRI at .95 Tesla by measuring changes in susceptibility and velocity. Magn. Reson. Med. 39 (1), 97–107) and (Menon, R.S., 2002. Postacquisition suppression of large-vessel BOLD signals in high-resolution fMRI. Magn. Reson. Med. 47 (1), 1–9) indicate that the voxel phase time course may exhibit task related changes. In this paper, a general complex fMRI activation model is introduced that describes both the magnitude and phase in complex data which can be used to specifically characterize task related change in both. Hypotheses regarding task related magnitude and/or phase changes are evaluated using derived activation statistics. It was found that the Rowe–Logan complex constant phase model strongly biases against voxels with task related phase changes and that the current very general complex linear phase model can be cast to address several different hypotheses sensitive to different magnitude/phase changes.