Ultra-Slow Single-Vessel BOLD and CBV-Based fMRI Spatiotemporal Dynamics and Their Correlation with Neuronal Intracellular Calcium Signals

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Concept of functional imaging of memory decline in Alzheimer’s disease

Functional imaging methods such as Positron Emission Tomography (PET) and functional Magnetic Resonance Imaging (fMRI) have contributed inestimably to the understanding of physiological cognitive processes in the brain in the recent decades. These techniques for the first time allowed the in vivo assessment of different features of brain function in the living human subject. It was therefore obvious to apply these methods to evaluate pathomechanisms of cognitive dysfunction in disorders such as Alzheimer’s disease (AD) as well. One of the most dominant symptoms of AD is the impairment of memory. In this context, the term “memory” represents a simplification and summarizes a set of complex cognitive functions associated with encoding and retrieval of different types of information. A number of imaging studies assessed the functional changes of neuronal activity in the brain at rest and also during performance of cognitive work, with regard to specific characteristics of memory decline in AD. In the current article, basic principles of common functional imaging procedures will be explained and it will be discussed how they can be reasonably applied for the assessment of memory decline in AD. Furthermore, it will be illustrated how these imaging procedures have been employed to improve early and specific diagnosis of the disease, to understand specific pathomechanisms of memory dysfunction and associated compensatory mechanisms, and to draw reverse conclusions on physiological function of memory.     

Hormone therapy does not modify emotion-induced brain activity in older women

Sex hormones have actions in brain regions important for emotion, including the amygdala and prefrontal cortex. Previous studies have shown that cyclic sex hormones and hormone therapy after menopause modify responses to emotional events. Thus, this study examined whether hormone therapy modified emotion-induced brain activity in older women. Functional magnetic resonance imaging (fMRI), behavioral ratings (valence and arousal), and recognition memory were used to assess responses to emotionally laden scenes in older women currently using hormone therapy (HT) and women not currently using hormone therapy (NONE). We hypothesized that hormones would affect the amount or persistence of emotion-induced brain activity in the amygdala and ventrolateral prefrontal cortex (VLPFC). However, hormone therapy did not affect brain activity with the exception that NONE women showed a modest increase over time in amygdala activity to positive scenes. Hormone therapy did not affect behavioral ratings or memory for emotional scenes. The results were similar when women were regrouped based on whether they had ever used hormone therapy versus had never used hormone therapy. These results suggest that hormone therapy does not modify emotion-induced brain activity, or its persistence, in older women.     

Identification and comparison of stochastic metabolic/hemodynamic models (sMHM) for the generation of the BOLD signal

This paper extends a previously formulated deterministic metabolic/hemodynamic model for the generation of blood oxygenated level dependent (BOLD) responses to include both physiological and observation stochastic components (sMHM). This adds a degree of flexibility when fitting the model to actual data by accounting for un-modelled activity. We then show how the innovation method can be used to estimate unobserved metabolic/hemodynamic as well as vascular variables of the sMHM, from simulated and actual BOLD data. The proposed estimation method allowed for doing model comparison by calculating the model’s AIC and BIC. This methodology was then used to select between different neurovascular coupling assumptions underlying sMHM. The proposed framework was first validated on simulations and then applied to BOLD data from a motor task experiment. The models under comparison in the analysis of the actual data considered that vascular response was coupled to: (I) inhibition, (II) excitation, (III) both excitation and inhibition. Data was best described by model II, although model III was also supported.     

fMRI mapping of cortical centers following visual stimulation in postnatal pigs of different ages

Classical studies have demonstrated that the visual centers in primates consist of cortical areas V1, V2 and V4 and their branches. However, nothing is known about how these visual areas change in postnatal development. In the present studies, therefore, pigs aged 2, 4, and 6 months old, were stimulated visually with a colored checker board and the active sites in the cortex, cerebellum and brainstem recorded using functional magnetic resonance imaging (fMRI). In pigs aged 2 months old, visual stimulation induced an increase in activation of sites in the V2 and V4 cortical areas, as well as in the areas of the inferior aspect of the parietal and middle aspect of the temporal cortices, but not in the medial and caudal occipital cortex (V1 area). At 4 months old, the V1 area was also activated, and by 6 months old, an inferior sector in the prefrontal cortex was also activated. As the pigs aged, functional active sites were further demonstrated in the cerebellum and the brainstem, which probably had to do with action memory, and the control of the ocular muscles, respectively. It is concluded that the visual pathway of the pig mainly involves cortical areas that mature at 6 months of age.     

Basics of Multivariate Analysis in Neuroimaging Data

Multivariate analysis techniques for neuroimaging data have recently received increasing attention as they have many attractive features that cannot be easily realized by the more commonly used univariate, voxel-wise, techniques^1,5,6,7,8,9. Multivariate approaches evaluate correlation/covariance of activation across brain regions, rather than proceeding on a voxel-by-voxel basis. Thus, their results can be more easily interpreted as a signature of neural networks. Univariate approaches, on the other hand, cannot directly address interregional correlation in the brain. Multivariate approaches can also result in greater statistical power when compared with univariate techniques, which are forced to employ very stringent corrections for voxel-wise multiple comparisons. Further, multivariate techniques also lend themselves much better to prospective application of results from the analysis of one dataset to entirely new datasets. Multivariate techniques are thus well placed to provide information about mean differences and correlations with behavior, similarly to univariate approaches, with potentially greater statistical power and better reproducibility checks. In contrast to these advantages is the high barrier of entry to the use of multivariate approaches, preventing more widespread application in the community. To the neuroscientist becoming familiar with multivariate analysis techniques, an initial survey of the field might present a bewildering variety of approaches that, although algorithmically similar, are presented with different emphases, typically by people with mathematics backgrounds. We believe that multivariate analysis techniques have sufficient potential to warrant better dissemination. Researchers should be able to employ them in an informed and accessible manner. The current article is an attempt at a didactic introduction of multivariate techniques for the novice. A conceptual introduction is followed with a very simple application to a diagnostic data set from the Alzheimer s Disease Neuroimaging Initiative (ADNI), clearly demonstrating the superior performance of the multivariate approach.