In this paper we propose to derive and represent the intrinsic functional geometry of a brain from functional magnetic resonance imaging (fMRI) data for a specific task. paper we address the question of synchronization within the brain. That is, we search for brain regions that exhibit highly coherent behavior as a strong indication of cooperation during an activity. We expect the BOLD signals corresponding to these regions to be observations stemming from a single source – the cooperative work caused by a certain condition. We propose a method to capture and represent these relationships in a transparent manner. It allows for data AR-42 (HDAC-42) manufacture exploration, and for quantitative measurements of relationships between different regions of the brain, which are task-specific and dynamic. We call the set of these relationships the to explore the functional geometry of a brain for a certain task or time period. Each spatial position in the brain is mapped to a position in the map that is governed by the functional coherence of the corresponding observed BOLD signals in the brain (Fig. 1). The model map is built by calculating a Markov chain with nodes representing the positions in the brain, and transition probabilities defined by the description lengths [7] of models, that encode the joint density of the signals. The resulting model map captures joint modeling behavior of signals acquired at different positions, and reflects this functional geometry. It has several interesting properties: functional relations are translated to Euclidean distance, groups of voxels therefore, that have a high probability to stem from the same model, form clusters in the map. The density AR-42 (HDAC-42) manufacture for positions in the map provides information about how a point is to any other region in the brain. High density indicates high coherence with many other signals, while low density indicates independent behavior relatively. These properties are essential for data exploration of FANCG complex fMRI sequences. Of a parcellation of the brain Instead, they present the entire functional geometry including subtle dependencies. The unique position of points in a comparison is made by the map between subjects, and between time-points for the same subject possible. These properties have considerable diagnostic value (as reported in the experiments), and we believe that they are an important tool, to explore and assess the changing distribution patterns of individual brain activities, that are not captured by the BOLD signal strength at individual positions. We evaluate the method on a challenging data set, that exhibits subtle cognitive changes regarding reward processing. Experiments show that the method is able to capture subtle differences, and interactions for different tasks. Fig. 1 Generating a model map generation from fMRI data 2 Model Maps to Find Geometry in Functional Brain Data We aim at a representation that maps measurements to positions in a space, so that low distance between two points indicates high compactness for a model that encodes both of them, or high temporal coherence of their signals. We derive model maps from a set of signals {x1,, x ?is the BOLD signal observed at one position in the brain for time points. In this section we will discuss how to define a similarity function first, that captures relations between BOLD signals based on a multivariate Gaussian model. We will describe how to construct a Markov chain Then, and the corresponding model map with new positions for each signal xof communicating a model ? itself (the parameters of the Gaussians) and the data (i.e. BOLD signals) encoded with the AR-42 (HDAC-42) manufacture model: examples, each consisting of BOLD signal observations for points, we derive a metric on the set of points, that reflects their joint modeling behavior. The construction of such a diffusion map is explained in detail.