One single voxels) becomes active during the execution

One of the most important method in cognitive neuroscience is fMRI. It comes from the early works of Ogawa and colleagues (1990) who first proposed the BOLD signal as a measure of neural activity. Since that moment, great progress has been made in fMRI in terms of hardware, acquisition protocols and data analysis. At first, researchers were focused on the classification of brain areas responding to particular stimuli or tasks, and they strived to replicate findings from neuroanatomy and neuropsychology. Afterwards, new data analysis concepts and implementations shifted the focus on the content of those task specific regions, the characterization of the representation of neuronal populations, and the communication between different areas. Mathematics, Statistics and Computer science have been playing a big role in this revolution providing several methods that enhanced our understanding of the brain structures and functions. They introduced innovative tools that are expanding the possibilities in many field of cognitive sciences. In this essay, I will discuss some of those novel approaches.

Multi-variate pattern analysis
A core concept of Neuroscience is that neurons work as an ensemble to shape our inner and outer world in order to create significant meanings necessary to our survival. We refer to it as neural representation and every single mental state is associated to a different  pattern of neural activity. “Representation links cognition to brain activity and enables us to build functional theories of brain information processing” (Kriegeskorte and Kievit, 2013). The variety of different possible activations is almost infinite. Just consider the 80 billion neurons, and their connections, and it is easy to understand how big and differentiate the representational space is. Moving from this idea recent analyses of neuronal recordings and functional imaging data have increasingly focused on patterns of activity within a functional region. Traditional analyses, known as activation-based or univariate analysis, aim to identify which region (or single voxels) becomes active during the execution of a specific task or group of voxels showing effects in the same direction, and to infer involvement of the region in a specific mental function. To some extent, this type of analysis are able to decode the content of brain activity and are well suited to study, for instance, ensemble category selectivity in visual cortex (Kamitani and Tong, 2005). One of the main feature of univariate analysis is that, to increase sensitivity and to find voxels responding to a particular condition, it spatially averages the activity across voxels responding to that condition. This approach enhances the signal-to-noise ratio, yet it causes some loss of information: (i) statistically non-significant active voxels are discarded and they might carry some information; (ii) due to spatial averaging is not possible to look at fine-grained differences between spatial patterns. Furthermore, one other limitation is the fact that with this approach voxels or regions are assumed to be independent. Actually, brain areas are not necessarily discrete and is important to consider and assess dependency between voxels. To address this issue, one innovative tool is multi-variate pattern analysis (MVPA). “Pattern-information analysis aims to detect activity-pattern differences and to infer representational content” (Mur et al. 2009). One of the first prominent studies, exploiting this method, provided evidence that information about category is not only localized within areas with the strongest activation but is widely distributed (and overlapping) across the visual cortex (Haxby, 2001). MVPA does not involve spatial averaging, but it treats each voxel as a distinct source of information about the information content of neuronal code. When no difference in regional-average activity is present, only MVPA can detect fine-grained differences within the region. As shown in a study by Raizada et al. (2008), two sounds of the same length (‘la’ vs ‘ra’) elicit the same amount of activity in primary auditory cortex when analyzed with standard fMRI analysis. However, using multi-variate analysis they were able to probe the information content of the region of interest, and they found that the two sounds are represented with discriminable patterns of activation. Generally, the term MVPA refers to two distinct approaches that exploit different analysis methods: classifier-based and similarity-based MVPA. 

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