Functional magnetic resonance imaging (fMRI) is widely used to study the operational organization of the human brain, but the exact relationship between the measured fMRI signal and the underlying neural activity is unclear. Here we present simultaneous intracortical recordings of neural signals and fMRI responses. We compared local field potentials (LFPs), single- and multi-unit spiking activity with highly spatio-temporally resolved blood-oxygen-level-dependent (BOLD) fMRI responses from the visual cortex of monkeys. The largest magnitude changes were observed in LFPs, which at recording sites characterized by transient responses were the only signal that significantly correlated with the haemodynamic response. Linear systems analysis on a trialby-trial basis showed that the impulse response of the neurovascular system is both animal- and site-specific, and that LFPs yield a better estimate of BOLD responses than the multi-unit responses. These findings suggest that the BOLD contrast mechanism reflects the input and intracortical processing of a given area rather than its spiking output.

/pdf/neurophysiological-investigation-of-the-basis-of-the-fmri-1pwsdtcobg.pdf

Neurophysiological investigation of the basis of the fMRI signal

https://cfmriweb.ucsd.edu/tliu/pdf/behzadi07_compcor.pdf

A component based noise correction method (CompCor) for BOLD and perfusion based fMRI

Inferring resting-state connectivity patterns from functional magnetic resonance imaging (fMRI) data is a challenging task for any analytical technique. In this paper, we review a probabilistic independent component analysis (PICA) approach, optimized for the analysis of fMRI data, and discuss the role which this exploratory technique can take in scientific investigations into the structure of these effects. We apply PICA to fMRI data acquired at rest, in order to characterize the spatio-temporal structure of such data, and demonstrate that this is an effective and robust tool for the identification of low-frequency resting-state patterns from data acquired at various different spatial and temporal resolutions. We show that these networks exhibit high spatial consistency across subjects and closely resemble discrete cortical functional networks such as visual cortical areas or sensory-motor cortex.

/pdf/investigations-into-resting-state-connectivity-using-2ztrvae9kb.pdf

Investigations into resting-state connectivity using independent component analysis

We present an integrated approach to probabilistic independent component analysis (ICA) for functional MRI (FMRI) data that allows for nonsquare mixing in the presence of Gaussian noise. In order to avoid overfitting, we employ objective estimation of the amount of Gaussian noise through Bayesian analysis of the true dimensionality of the data, i.e., the number of activation and non-Gaussian noise sources. This enables us to carry out probabilistic modeling and achieves an asymptotically unique decomposition of the data. It reduces problems of interpretation, as each final independent component is now much more likely to be due to only one physical or physiological process. We also describe other improvements to standard ICA, such as temporal prewhitening and variance normalization of timeseries, the latter being particularly useful in the context of dimensionality reduction when weak activation is present. We discuss the use of prior information about the spatiotemporal nature of the source processes, and an alternative-hypothesis testing approach for inference, using Gaussian mixture models. The performance of our approach is illustrated and evaluated on real and artificial FMRI data, and compared to the spatio-temporal accuracy of results obtained from classical ICA and GLM analyses.

/pdf/probabilistic-independent-component-analysis-for-functional-2mvcmrc5z0.pdf

Probabilistic independent component analysis for functional magnetic resonance imaging

http://mriquestions.com/uploads/3/4/5/7/34572113/language_review1-s2.0-s1053811912004703-main.pdf

A review and synthesis of the first 20 years of PET and fMRI studies of heard speech, spoken language and reading

The physiological noise in the resting brain, which arises from fluctuations in metabolic-linked brain physiology and subtle brain pulsations, was investigated in six healthy volunteers using oxygenation-sensitive dual-echo spiral MRI at 3.0 T. In contrast to the system and thermal noise, the physiological noise demonstrates a signal strength dependency and, unique to the metabolic-linked noise, an echo-time dependency. Variations of the MR signal strength by changing the flip angle and echo time allowed separation of the different noise components and revealed that the physiological noise at 3.0 T (1) exceeds other noise sources and (2) is significantly greater in cortical gray matter than in white matter regions. The SNR in oxygenation-sensitive MRI is predicted to saturate at higher fields, suggesting that noise measurements of the resting brain at 3.0 T and higher may provide a sensitive probe of functional information.

Physiological noise in oxygenation-sensitive magnetic resonance imaging

Noise properties, the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and signal responses were compared during functional activation of the human brain at 1.5 and 3.0 T. At the higher field spiral gradient-echo (GRE) brain images revealed an average gain in SNR of 1.7 in fully relaxed and 2.2 in images with a repetition time (TR) of 1.5 sec. The tempered gain at longer TRs reflects the fact that the physiological noise depends on the signal strength and becomes a larger fraction of the total noise at 3.0 T. Activation of the primary motor and visual cortex resulted in a 36% and 44% increase of "activated pixels" at 3.0 T, which reflects a greater sensitivity for the detection of activated gray matter at the higher field. The gain in the CNR exhibited a dependency on the underlying tissue, i.e., an increase of 1.8x in regions of particular high activation-induced signal changes (presumably venous vessels) and of 2.2x in the average activated areas. These results demonstrate that 3.0 T provides a clear advantage over 1.5 T for neuroimaging of homogeneous brain tissue, although stronger physiological noise contributions, more complicated signal features in the proximity of strong susceptibility gradients, and changes in the intrinsic relaxation times may mediate the enhancement. Magn Reson Med 45:595-604, 2001.

Neuroimaging at 1.5 T and 3.0 T: comparison of oxygenation-sensitive magnetic resonance imaging.

Changes in glucose consumption, lactate production, and blood oxygenation were measured during prolonged neuronal activation (4–6 min) in human primary visual cortex using dynamic magnetic resonance spectroscopy and imaging. A decrease of steady-state glucose by 40% because of enhanced use by 21% was accompanied by a transient accumulation of lactate with a peak value of 170% 2.5 min after stimulation onset Rapid blood hyperoxygenation indicating „uncoupling” of blood flow and oxidative metabolism was followed by a return to basal levels over 3 min. Thus, initial nonoxidative glucose consumption during functional activation is gradually complemented by a slower adjustment of oxidative phosphorylation that „recouples” perfusion and oxygen consumption at a new equilibrium.

Gunnar Krüger

Papers

Physiological noise in oxygenation-sensitive magnetic resonance imaging

Neuroimaging at 1.5 T and 3.0 T: comparison of oxygenation-sensitive magnetic resonance imaging.

Dynamic uncoupling and recoupling of perfusion and oxidative metabolism during focal brain activation in man.

Assessment of cerebrovascular reactivity with functional magnetic resonance imaging: comparison of CO2 and breath holding

Regional variability of cerebral blood oxygenation response to hypercapnia.