Recolección de datos

The idea of applying multivariate statistics or machine learning classifiers to analyze multidimensional data obtained from various neuroscience experiments is not new. In an early PET imaging study, Kippenhan et al. (1992) applied a neural-network classifier for discriminating normal and abnormal scans in relation to Alzheimer disease. They found that despite the low resolution of PET images, the performance of the classifier was nearly comparable to the opinions of human experts. Similar application of a neural-network classifier combined with various dimensionality reduction methods (PCA, SVD) on PET scans was also reported by Lautrup et al. (1994). Another multivariate method which has been successfully applied in early functional neuroimaging research to correlate brain activity

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with experimental design was Partial Least Squares (PLS), a statistical technique originally developed for econometrics and chemometrics (McIntosh et al., 1996). Although this method is not as prevalent as other multivariate techniques that were more commonly used in neuroimaging, such as correlation or SVM, various studies have demonstrated its power to tackle various problems in cognitive and clinical neuroimaging (see Krishnan et al., 2011 for review).

Within the neuroimaging community, an interest in multivariate classification methods was renewed in the early 21st century by a pivotal work of Haxby et al. (2001). In their study, they showed that the pattern of brain activity in the ventral temporal (VT) cortex could reliably discriminate multiple object categories viewed by subjects (Figure 2.1). This categorical discrimination cannot be simply explained by differences in the intensities of voxels that responded strongly to one category relative to the other, because when these voxels were removed, discrimination performance remained well above chance. This combination of multivariate classification and „virtual lesion‟ type of analysis suggests that neural representations of objects and faces were probably overlapping and distributed throughout the VT region. Although the conclusion of this finding has been challenged by subsequent studies which also utilized multivariate methods (Spiridon and Kanwisher, 2002; Peelen and Downing, 2007), nonetheless it has opened up a new vista to creatively apply multivariate classification methods to tackle difficult and controversial problems in neuroscience.

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Figure 2.1. Example of correlation approach in brain activity decoding (adapted from Haxby et al.,

2001). Overlaid on top of the brain images are normalized patterns of response for faces and houses in the ventral temporal cortex, measured separately for even and odd runs. Pairwise correlations within an object category and between two object categories were calculated between even and odd runs. Decoding was considered successful if the within-category correlations (r = 0.81 for faces, and r = 0.87 for houses) were larger than the between-category correlations (r = -0.4 and r = -0.47).

Another pioneering study that pushed forward the application of multivariate analysis methods was conducted by Kamitani and Tong (2005). In their study, they trained an ensemble of eight linear support vector machines to classify eight different orientation of grating based on the activity of visual cortex. After being trained, the classifiers were employed to predict the orientation of newly viewed gratings by choosing the orientation that corresponded to the classifier with the maximal output. They found that it was possible to decode orientation of the observed stimulus in visual cortex with surprisingly high accuracy (Figure 2.2b), even though orientation- and direction-selective neurons in visual cortex were topographically organized at the scale of sub-millimeter columns (Bartfeld and Grinvald, 1992; Obermayer and Blasdel, 1993), much smaller than the resolution of the voxel itself (3mm).

Boynton (2005) performed a simulation to explain that random irregularities in the distribution of orientation selective neurons could give rise to small orientation bias in each voxel; thus by aggregating information across multiple neighbouring voxels, the pattern classifier was able to robustly decode orientation of the grating from the activity of visual cortex (Figure 2.2a). A subsequent study using high resolution fMRI by Swisher et al. (2010) also supported this voxel bias hypothesis (Figure 2.2c). In a replication and extension of Kamitani and Tong (2005), Haynes and Rees (2005) reported above-chance decoding accuracy in visual cortex, despite the fact that the subjects were rendered unaware of the orientation of the gratings through backward masking. This finding provides initial evidence of the presence of unconscious information in the primary visual cortex.

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Figure 2.2. (a) Illustration of how organization of the visual cortex gave rise to slight orientation bias

in the voxels activity (adapted from Boynton, 2005). The panel on the left shows a simulated orientation tuning map with a voxel size of 3 mm, with each colour represent different orientation. The histogram on the right panel shows the distribution of orientation selectivity values for each of the voxels shown in the left panel. Eight different orientations were indicated by different coloured line, shown below the panel. The shape of the distribution is slightly different for each voxel and thus biases the response of each voxel differently from the others. The learning algorithm is able to exploit this random variability to decode line orientations from patterns of activity in multiple voxels. (b) MVPA decoding of the oriented gratings from activity patterns in V1/V2 (adapted from Kamitani and Tong, 2005). The polar plots indicate the distribution of the predicted orientations for each of the eight orientations. Solid black lines indicate the true orientation. (c) Orientation preference map in the visual cortex measured using high-resolution fMRI with 1 mm voxel resolution and rendered on an inflated cortical surface (adapted from Swisher et al., 2010).

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The interest in applying multivariate analyses of neuroimaging data has grown exponentially during the last decade partly due to the realization that fMRI data analysis can be framed as a pattern classification problem; a type of problem which is also actively studied in statistics, machine learning, and data mining fields (Bishop, 2006; Mitchell, 2006; Hastie et al., 2009; Pereira et al., 2009). This approach enables neuroscientists to apply various classification algorithms that have been well developed and extensively used in other domains to analyze functional imaging data (Norman et al., 2006). Many commonly used pattern classification algorithms have been successfully applied, including linear Support Vector Machines (SVM) (Cox and Savoy, 2003; Kamitani and Tong, 2005; Esterman et al., 2009; Greenberg et al., 2010; Kalberlah et al., 2011), Fisher‟s Linear Discriminant Analysis (LDA) (Carlson et al., 2003; O‟Toole et al., 2005; Haynes and Rees, 2005), Neural Network (Polyn et al., 2005), and Gaussian Naïve Bayes classifiers (Mitchell et al., 2004). More recently, several labs have also published open source software libraries for analyzing multivariate neuroimaging data, such as MVPA toolbox released by Princeton lab (Detre et al., 2006) and a Python based library, PyMVPA (Hanke et al., 2009). These libraries greatly simplify the complexity of applying various sophisticated machine learning algorithms to process neuroimaging data, thus enabling neuroscientists to save time and focus more on the analysis rather than technical details of implementation.