Linear relationships between synaptic activity and hemodynamic responses are critically dependent on functional signal etiology and paradigm. To investigate these relationships, we simultaneously measured local field potentials (FPs) and optical intrinsic signals in rat somatosensory cortex while delivering a small number of electrical pulses to the hindpaw with varied stimulus intensity, number, and interstimulus interval. We used 570 and 610 nm optical signals to estimate cerebral blood volume (CBV) and oxygenation, respectively. The spatiotemporal evolution patterns and trial-by-trial correlation analyses revealed that CBV-related optical signals have higher fidelity to summed evoked FPs (SigmaFPs) than oxygenation-derived signals. CBV-related signals even correlated with minute SigmaFP fluctuations within trials of the same stimulus condition. Furthermore, hemodynamic signals (CBV and late oxygenation signals) increased linearly with SigmaFP while varying stimulus number, but they exhibited a threshold and steeper gradient while varying stimulus intensity, suggesting insufficiency of the homogeneity property of linear systems and the importance of spatiotemporal coherence of neuronal population activity in hemodynamic response formation. These stimulus paradigm-dependent linear and nonlinear relationships demonstrate that simple subtraction-based analyses of hemodynamic signals produced by complex stimulus paradigms may not reflect a difference in SigmaFPs between paradigms. Functional signal- and paradigm-dependent linearity have potentially profound implications for the interpretation of perfusion-based functional signals.

Cortical spreading depression (CSD) is a pronounced depolarization of neurons and glia that spreads slowly across the cortex followed by a period of depressed electrophysiological activity. The vascular changes associated with CSD are a large transient increase in blood flow followed by a prolonged decrease lasting greater than I h. Currently, the profile of functional vascular activity during this hypovolemic period has not been well characterized. Perfusion-based imaging techniques such as functional magnetic resonance imaging (fMRI) assume a tight coupling between changes in neuronal and vascular activity. Under normal conditions, these variables are well correlated. Characterizing the effect of CSD on this relationship is an important step to understand the impact acute pathophysiological events may have on neurovascular coupling. We examine the effect of CSD on functional changes in cerebral blood volume (CBV) evoked by cortical electrophysiological activity for I h following CSD induction. CBV signal amplitude, duration, and time to peak show little recovery at 60 min post-induction. Analysis of spontaneous vasomotor activity suggests a decrease in vascular reactivity may play a significant role in the disruption of normal functional CBV responses. Electrophysiological activity is also attenuated but to a lesser degree. CBV and evoked potentials are not well correlated following CSD, suggesting a breakdown of the neurovascular coupling relationship. (c) 2005 Society of Photo-Optical Instrumentation Engineers.

Cortical neurons with similar properties are grouped in columnar structures and supplied by matching vascular networks. The hemodynamic response to neuronal activation, however, is not well described on a fine spatial scale. We investigated the spatiotemporal characteristics of microvascular responses to neuronal activation in rat barrel cortex using optical intrinsic signal imaging and spectroscopy. Imaging was performed at 570 nm to provide functional maps of cerebral blood volume (CBV) changes and at 610 nm to estimate oxygenation changes. To emphasize parenchymal rather than large vessel contributions to the functional hemodynamic responses, we developed an ANOVA-based statistical analysis technique. Perfusion-based maps were compared with underlying neuroanatomy with cytochrome oxidase staining. Statistically determined CBV responses localized accurately to individually stimulated barrel columns and could resolve neighboring columns with a resolution better than 400 mum. Both CBV and early oxygenation responses extended beyond anatomical boundaries of single columns, but this vascular point spread did not preclude spatial specificity. These results indicate that microvascular flow control structures providing targeted flow increases to metabolically active neuronal columns also produce finely localized changes in CBV. This spatial specificity, along with the high contrast/noise ratio, makes the CBV response an attractive mapping signal. We also found that functional oxygenation changes can achieve submillimeter specificity not only during the transient deoxygenation ("initial dip") but also during the early part of the hyperoxygenation. We, therefore, suggest that to optimize hemodynamic spatial specificity, appropriate response timing ( using less than or equal to2-3 sec changes) is more important than etiology (oxygenation or volume).

We propose a new method for statistical analysis of functional magnetic resonance imaging (fMRI) data. The discrete wavelet transformation is employed as a tool for efficient and robust signal representation. We use structural magnetic resonance imaging (MRI) and fMRI to empirically estimate the distribution of the wavelet coefficients of the data both across individuals and spatial locations. An anatomical subvolume probabilistic atlas is used to tessellate the structural and functional signals into smaller regions each of what is processed separately. A frequency-adaptive wavelet shrinkage scheme is employed to obtain essentially optimal estimations of the signals in the wavelet space. The empirical distributions of the signals on all the regions are computed in a compressed wavelet space. These are modeled by heavy-tail distributions because their histograms exhibit slower tail decay than the Gaussian. We discovered that the Cauchy, Bessel K Forms, and Pareto distributions provide the most accurate asymptotic models for the distribution of the wavelet coefficients of the data. Finally, we propose a new model for statistical analysis of functional MRI data using this atlas-based wavelet space representation. In the second part of our investigation, we will apply this technique to analyze a large fMRI dataset involving repeated presentation of sensory-motor response stimuli young, elderly, and demented subjects.