A very important metric is the degree of distribution of a graph P(k). This metric provides information on the number of nodes that offer a high degree of centrality. Various studies

these nodes. Degree of a node is defined as the number edges that are connected to it.

optimal number of groups for that data set.

**3.3. Brain networks and graph theory**

well as being tolerant to failures.

characterize the graph as a whole.

modularity.

### **3.2. Model-free functional connectivity methods**

By definition, hypothesis independent methods lack a priori assumptions. These methods are especially useful in the analysis of spatially distributed functional connectivity networks, as they are not reliant of localized assumptions [15, 28-36].

The first analysis to study the functional integration used **Principal Component Analysis** (PCA) [15, 37] to decompose fMRI data into a set of mutually uncorrelated components in either space or time. More recently, **Independent Component Analysis** (ICA) has been used to identify components that describe the activity in a widely dispersed network [28-35]. ICA is an extension of the classical methods of Blind Source Separation (BSS). This technique decomposes the time series in order to identify statistically independent components that define functional networks. This technique has the advantage of extracting independent components that may consist of noise signal, such as physiological or movement-related noise. Although this analysis has many advantages and its applicability has opened doors to new possibilities in the study design, the maps generated with this method are usually more difficult to explain in comparison with those generated with the seed voxel analysis, therefore, its applicability has some restrictions and limitations. Different authors have analyzed the methodology of this technique and there are different variations of the method [38, 39]. ICA has been used to explain a better way the large-scale structures that have been detected in several independent studies [15, 36].

**Clustering techniques** aim to subdivide the data by means of a mathematical algorithm, so that the observations assigned to the same group are more similar to each other than the observations assigned to other groups. In the context of the study of functional connectivity analysis in rs-fMRI, clustering algorithms have been used to perform brain parcellation in groups of voxels or regions that are functionally connected to other regions. It should be noted that clustering techniques applied to fMRI data are having a good acceptance both in the detection of functional connectivity networks and in the architectural (anatomical) subdivision of the brain [36].

The first application of clustering techniques to resting state functional data was performed by Cordes et al [40]. Firsts attempts to employ this technique were limited due to the computational complexity [41, 42]. Recently, more sophisticated studies have been performed [43, 44] that have enabled identification of large-scale networks that agree with those found in other studies [44].

Still, we should take into account some limitations to the technique. The most important is that most clustering techniques require a priori selection of the number of clusters (K), which will partition the data [36]. Since this value clustering (K) is unknown, multiple solutions are usually calculated using a metric that predicts the "goodness" to determine the optimal number of groups for that data set.

There is no single measure or an optimized solution, so different methods are used. Ultimately, it is unlikely that the clustering is entirely independent of the initial partition decision (K) determined by the subjectivity of the user, since it must evaluate the appropriateness of pooling the results compared with networks of functional connectivity well known. It can be concluded, therefore, that this technique also depends on the subjectivity of the observer when describing the goodness of a grouping, as well as ICA.

#### **3.3. Brain networks and graph theory**

56 Novel Frontiers of Advanced Neuroimaging

**3.2. Model-free functional connectivity methods**

as they are not reliant of localized assumptions [15, 28-36].

detected in several independent studies [15, 36].

subdivision of the brain [36].

those found in other studies [44].

The goal of this analysis is to locate regions consistently activated (if any) on a set of diverse and independent studies, which are related to the same psychological state [26]. This method works basically by counting the number of peaks of activation in the studies presented, comparing the number of peaks observed with a distribution of null to set a criterion of significance. The two most representative approaches to perform such an analysis are kernel density analysis (KDA) and activation likelihood estimation (ALE) [27].

By definition, hypothesis independent methods lack a priori assumptions. These methods are especially useful in the analysis of spatially distributed functional connectivity networks,

The first analysis to study the functional integration used **Principal Component Analysis** (PCA) [15, 37] to decompose fMRI data into a set of mutually uncorrelated components in either space or time. More recently, **Independent Component Analysis** (ICA) has been used to identify components that describe the activity in a widely dispersed network [28-35]. ICA is an extension of the classical methods of Blind Source Separation (BSS). This technique decomposes the time series in order to identify statistically independent components that define functional networks. This technique has the advantage of extracting independent components that may consist of noise signal, such as physiological or movement-related noise. Although this analysis has many advantages and its applicability has opened doors to new possibilities in the study design, the maps generated with this method are usually more difficult to explain in comparison with those generated with the seed voxel analysis, therefore, its applicability has some restrictions and limitations. Different authors have analyzed the methodology of this technique and there are different variations of the method [38, 39]. ICA has been used to explain a better way the large-scale structures that have been

**Clustering techniques** aim to subdivide the data by means of a mathematical algorithm, so that the observations assigned to the same group are more similar to each other than the observations assigned to other groups. In the context of the study of functional connectivity analysis in rs-fMRI, clustering algorithms have been used to perform brain parcellation in groups of voxels or regions that are functionally connected to other regions. It should be noted that clustering techniques applied to fMRI data are having a good acceptance both in the detection of functional connectivity networks and in the architectural (anatomical)

The first application of clustering techniques to resting state functional data was performed by Cordes et al [40]. Firsts attempts to employ this technique were limited due to the computational complexity [41, 42]. Recently, more sophisticated studies have been performed [43, 44] that have enabled identification of large-scale networks that agree with In 1998 Watts and Strogatz introduced the concept of small-world network [45]. There are certain reasons, both empirical and theoretical, to understand the brain as a small-world network [46] because the brain supports both distributed and modular processing (linked to the concepts of functional segregation and functional integration). Considering the cognitive processes under the network architecture, it is more efficient when exchanging information at various scales: a high clustering allows a modular processing, while further distances allow for distributed processing. Thus, small-world networks maximize the efficiency of parallel processing and minimize the cost of communication between modules of nodes, as well as being tolerant to failures.

**Graph theory** is the field of mathematics that is used to characterize various aspects of network structure. In functional connectivity analysis, the application of graph theory aims to ascribe nodes to various regions of interest, and generates links or arcs between them. This approach makes it possible to explore functional connectivity networks using tools that characterize typical properties of networks, for example the study of efficiency and modularity.

Applying graph theory to resting state fMRI data involves assigning nodes to various regions of interest. A functional connectivity graph is generated once edges are assigned to connect nodes that have correlation values above a certain threshold. An important feature is to study "the path", which is defined as a sequence of connected nodes. Path length between two nodes is defined as the number of edges passing through node i to a node j. The distance between two nodes of a graph is the minimum length among all possible paths connecting these nodes. Degree of a node is defined as the number edges that are connected to it.

Two types of metrics are used to characterize graphs: local and global metrics. In local metrics, values are assigned to each node individually, while in global metrics, values characterize the graph as a whole.

A very important metric is the degree of distribution of a graph P(k). This metric provides information on the number of nodes that offer a high degree of centrality. Various studies

have shown that the scarcity of these nodes can be related to cognitive decline as observed in Alzheimer's patients.

Brain Connections – Resting State fMRI Functional Connectivity 59

**4. RS-fMRI in cognitive neuroscience**

**4.1. Adquisition**

neuroscience studies include:

It was in 1995 when Biswal and colleagues observed that a significant fraction of supposed noise showed organized patterns consistent with known brain systems [5]. Biswal's work aimed to examine patterns of neural activity of the motor system, and for that, experimental subjects were asked for a standard task of finger opposition and then compared to a rs-fMRI without asking them to do something. As Biswal initially demonstrated, the left and right primary motor networks are correlated. This suggested that these areas are functionally connected and that the process of information transfer between them is on-going [7, 39]. Later, groups were able to replicate these results and further demonstrate correlations between primary visual networks, auditory networks and higher order cognitive networks [3,7, 48-51].

Advantages of employing resting state functional connectivity analysis in cognitive

1. **A brief period of time of acquisition** (minutes) provides an expanded application to the clinical setting. One of the most frequently cited motivations for using resting state functional connectivity in clinical studies, is that it allows for increased sampling of patient populations, since (i) it requires a brief acquisition period and (ii) has no specific stimulation paradigm. This allows the patient to remain rest, or asleep or under the influence of anesthesia. The absence of a stimulation paradigm in this case, is especially important to the sampling of patient populations with neuropsychiatric disorders. 2. **Computation**: Several resting state functional connectivity methods are available for analysis of neural circuitry in vivo. In contrast, using task-based fMRI, we are only

3. **Simple design**: (baseline acquisition). Resting state studies may offer a better signal to

A resting state network of particular interest is the **default mode network (DMN).** This group of brain regions is active during rest and deactivates during most externally oriented tasks [7, 52]. This component has been studied in different ways, the main interest of past studies that implemented model-dependent method (seed voxel techniques) [5, 52] as well

Compared with other networks, the DMN is unique in the direction of its response to task performance, which probably relates to its baseline level of neuronal and metabolic activity and its role in brain function. The DMN is not unique, however, in demonstrating correlated intrinsic activity; multiple networks exhibit coherent resting state activity that persists across different states. The default mode network is one of the most robustly identified and extensively investigated resting state networks that involves a set of regions that routinely decrease their activity with tasks that demand attention [3, 7, 52]. Interestingly, this network

afforded connectivity information about the activated regions only.

**4.2. Networks identified using resting state functional connectivity**

noise ratio than conventional task-based approaches.

as independent component analysis (ICA) [29].

A complex network can be represented mathematically by edges and nodes [47]. Mathematically, nodes represent different parts of a system, and the relationship between two nodes is represented by an edge. Applying these mathematical concepts to the brain, nodes represent different areas of the brain, while edges represent anatomical, functional or effective connectivity's between these nodes. Utilizing such tools, we can construct (i) anatomically based networks of white matter tracts; (ii) functional connectivity maps representing patterns of correlation between BOLD signals; and (iii) effective networks representing causal interaction patterns between brain areas. All three methods of network depiction can be represented by adjacency matrices. In these matrices, rows and columns represent nodes, while each array element (i, j) represents the interaction between two nodes i, j.

Criteria for the selection of nodes and edges to represent cortical networks often combine methods from anatomical parcellation schemes and connectivity measures. Nodes must represent brain regions with consistent patterns of connectivity, since only similar patterns of fragmentations can be compared. Weighting of edges is interpreted differently in different connectivity models. In anatomical connectivity modelling, weighting is interpreted as a measure of the density of the anatomical tracts. While in functional connectivity models, weighting indicates the magnitude of a correlation between brain areas. And in effective connectivity models, weights may indicate causal interactions. By thresholding weights of connections, networks could be trimmed to establish the topology of significant links. The possibility of directionality in the edges makes it possible to represent anatomical and effective models, while functional models can be perfectly modelled by non-directional arcs.

Functional segregation refers to the involvement of specialized regions or networks in specific functions. Segregation measures are important as they seek to quantify clusters in given network. A basic strategy is to divide a network into subgroups, minimizing the number of edges within a group and maximizing the number of edges outside of a group. Utilizing such means, we can divide a network to be able to analyse a networks modular structure.

Certain areas of the brain that act as hubs are crucial to the functionality of given networks. In order to quantify this importance there are measures of centrality for the network. The degree of a node can be used as a simple measure of the centrality of a node. Other more sophisticated measures can also be used to quantify centrality. Many centrality measures are based on important hubs that play a role in various networks. Measures of centrality have different interpretations depending on type of network under study. A central node in an anatomical network allows the assignment of structure to function between distant regions. Such central anatomic nodes decrease values of centrality in functional networks.

Here we conclude that the analysis of complex networks has equipped use with necessary tools to examine anatomical and functional networks of the brain, paving the way for the possibility of quantifying many of their parameters.

### **4. RS-fMRI in cognitive neuroscience**

It was in 1995 when Biswal and colleagues observed that a significant fraction of supposed noise showed organized patterns consistent with known brain systems [5]. Biswal's work aimed to examine patterns of neural activity of the motor system, and for that, experimental subjects were asked for a standard task of finger opposition and then compared to a rs-fMRI without asking them to do something. As Biswal initially demonstrated, the left and right primary motor networks are correlated. This suggested that these areas are functionally connected and that the process of information transfer between them is on-going [7, 39]. Later, groups were able to replicate these results and further demonstrate correlations between primary visual networks, auditory networks and higher order cognitive networks [3,7, 48-51].

### **4.1. Adquisition**

58 Novel Frontiers of Advanced Neuroimaging

modelled by non-directional arcs.

structure.

in Alzheimer's patients.

have shown that the scarcity of these nodes can be related to cognitive decline as observed

A complex network can be represented mathematically by edges and nodes [47]. Mathematically, nodes represent different parts of a system, and the relationship between two nodes is represented by an edge. Applying these mathematical concepts to the brain, nodes represent different areas of the brain, while edges represent anatomical, functional or effective connectivity's between these nodes. Utilizing such tools, we can construct (i) anatomically based networks of white matter tracts; (ii) functional connectivity maps representing patterns of correlation between BOLD signals; and (iii) effective networks representing causal interaction patterns between brain areas. All three methods of network depiction can be represented by adjacency matrices. In these matrices, rows and columns represent nodes,

Criteria for the selection of nodes and edges to represent cortical networks often combine methods from anatomical parcellation schemes and connectivity measures. Nodes must represent brain regions with consistent patterns of connectivity, since only similar patterns of fragmentations can be compared. Weighting of edges is interpreted differently in different connectivity models. In anatomical connectivity modelling, weighting is interpreted as a measure of the density of the anatomical tracts. While in functional connectivity models, weighting indicates the magnitude of a correlation between brain areas. And in effective connectivity models, weights may indicate causal interactions. By thresholding weights of connections, networks could be trimmed to establish the topology of significant links. The possibility of directionality in the edges makes it possible to represent anatomical and effective models, while functional models can be perfectly

Functional segregation refers to the involvement of specialized regions or networks in specific functions. Segregation measures are important as they seek to quantify clusters in given network. A basic strategy is to divide a network into subgroups, minimizing the number of edges within a group and maximizing the number of edges outside of a group. Utilizing such means, we can divide a network to be able to analyse a networks modular

Certain areas of the brain that act as hubs are crucial to the functionality of given networks. In order to quantify this importance there are measures of centrality for the network. The degree of a node can be used as a simple measure of the centrality of a node. Other more sophisticated measures can also be used to quantify centrality. Many centrality measures are based on important hubs that play a role in various networks. Measures of centrality have different interpretations depending on type of network under study. A central node in an anatomical network allows the assignment of structure to function between distant regions.

Here we conclude that the analysis of complex networks has equipped use with necessary tools to examine anatomical and functional networks of the brain, paving the way for the

Such central anatomic nodes decrease values of centrality in functional networks.

possibility of quantifying many of their parameters.

while each array element (i, j) represents the interaction between two nodes i, j.

Advantages of employing resting state functional connectivity analysis in cognitive neuroscience studies include:

#### **4.2. Networks identified using resting state functional connectivity**

A resting state network of particular interest is the **default mode network (DMN).** This group of brain regions is active during rest and deactivates during most externally oriented tasks [7, 52]. This component has been studied in different ways, the main interest of past studies that implemented model-dependent method (seed voxel techniques) [5, 52] as well as independent component analysis (ICA) [29].

Compared with other networks, the DMN is unique in the direction of its response to task performance, which probably relates to its baseline level of neuronal and metabolic activity and its role in brain function. The DMN is not unique, however, in demonstrating correlated intrinsic activity; multiple networks exhibit coherent resting state activity that persists across different states. The default mode network is one of the most robustly identified and extensively investigated resting state networks that involves a set of regions that routinely decrease their activity with tasks that demand attention [3, 7, 52]. Interestingly, this network

has also been found to be negatively correlated with regions that tend to increase their activity during attention demanding tasks. Other identified networks include a self-referential system engaging the medial prefrontal regions; a posterior network involved in visual processing; an attention network engaging superior frontal and parietal cortex; a superior temporal system; and a network-engaging precentral and postcentral cortex [3, 7]. The most consistently reported resting state networks include the primary sensorimotor network; the primary visual and extra-striate visual network consisting of bilateral temporal/insular and anterior cingulate cortex regions; left and right lateralized networks consisting of superior parietal and superior frontal regions; and the default mode network consisting of precuneus, medial frontal, inferior parietal cortical regions and medial temporal lobe [53].

Brain Connections – Resting State fMRI Functional Connectivity 61

schizophrenia can be attributed to alterations of the functional connectivity of the DMN. Moreover, studies have also marked spatial differences in the default mode network in schizophrenia patients together with significant higher frequency fluctuations in default mode regions, as well as hyperactivity and hyperconnectivity of the default mode network in patients in the early phase of schizophrenia [64]. These studies suggest an important role

Altered functional connectivity patterns have been reported in other neurological disorders. For example, in **multiple sclerosis (MS)** a decreased functional connectivity in the primary motor has been established. This has substantiated studies that showed decreased microstructural integrity of the callosal white matter tracts [66]. Additionally, in patients with amyotrophic lateral sclerosis (ALS), ICA analysis has suggested a decrease in

There has been a long standing interest in the unrestricted sharing and access of functional neuroimaging data within the neuroimaging community. Inherent within the methodology of task based fMRI, the complexity of conforming task paradigms across different acquisition sites limited the potential of having such an open access platform. However, with the introduction of the unique methodological approach of resting state fMRI, a new era in open access data sharing was ushered. Most notably, a data sharing consortium - the 1000 Functional Connectomes project (fcon\_1000.projects.nitrc.org) - composed of various groups from all around began this initiative in 2009. The fcon1000 project aggregated and publicly released over 1200 resting state and anatomical MRI datasets acquired at 33 sites around the world. Within the first six months, the release generated over 9000 download from 78 countries. The benefit of unrestricted data sharing on the advancement of neuroimaging is self-evident. Researchers with the resources to acquire data can ensure that their resources are most widely used, while those researchers who prefer to focus on data analysis can do so. Such initiatives had wide implications on the neuroimaging community. Since large sets of data are now freely accessible, the valued commodity of the imaging community is no longer the data itself, but the analytic tools and approaches of interpretation and analysis. This new paradigm of unrestricted data access will be an integral part of the future of brain research. The human brain is usually divided into several hundred areas that exhibit highly specialized function. Cortical and subcortical regions of divergent function exhibit distinct cytoarchitecture when viewed under a microscope. As the human brain develops, mechanisms of axonal guidance allow the projection of millions of axonal fibbers to target destinations. In the process, a complex network of information

The Human Connectome Project (HCP) [68] is an ambitious five year initiative that was launched in 2010. Its main objective is to build a network map of the human brain to shed light on its structural and functional connectivity. HCP leans on the general facet that the function of a system is determined by its structure. By elucidating the complete underlying

for the default mode network in the pathophysiology of schizophrenia.

**5. Global initiatives and the Human Connectome Project**

functional connectivity [67].

processing pathways is formed.

Given the success of resting state functional connectivity for probing the brain's functional architecture in normal subjects, it is a great benefit to employ this technique towards the investigation of dysfunction in the brain. A review by Fox and Greicius [8] highlights advantages of examining the resting state signal for clinical applications and discusses methodological issues that need to be resolved to facilitate translational applications of rsfMRI. A number of clinical applications are already emerging as emphasized by the studies of functional connectivity in premature children [54], adolescents with schizotypal traits [55], major depression, and aging [56]. Two recent reviews detail the large number of studies that have utilized resting state fcMRI to study various neurological and psychiatric conditions [4, 57].

#### **4.3. Networks involved in neurological and psychiatric diseases**

The functional connectivity of the DMN has been linked to core process of human cognition such as the integration of cognitive and emotional processing [7], monitoring [58] and mindwandering. As a result, analysis of the connectivity patterns of the DMN is especially important in examining cognitive dysfunction in neurological and neuropsychiatric brain disorders [57, 59]. DMN alterations have been reported in a number of neuropsychiatric diseases.

It has been shown that the DMN exhibits decreased connectivity patters in patients with **Alzheimer disease** [60]**,** decreased correlations within the DMN including hippocampi, decreased anticorrelations with the DMN, and reduced local connectivity as reflected in clustering coefficients, in which parts of this network have been clearly implicated.

**Schizophrenia** has been marked as a potential disconnection disease [61]. Widespread functional disconnectivity between brain regions has been suggested to underlie these symptoms [62, 63]. Schizophrenia is known to have aberrant effects on frontal and parietal regions involved with the DMN. Resting state fMRI studies have reported a decrease in the functional connectivity between medial frontal cortex and precuneus in schizophrenic patients [64]. Additionally, diffusion tensor imaging studies have reported diminished white matter integrity in patients with schizophrenia [65]. Specifically, a decrease in white matter integrity was observed in the cingulum tract, which is known to be interconnected with DMN regions MFC and PCC [50, 57]. Therefore, symptoms of patients with schizophrenia can be attributed to alterations of the functional connectivity of the DMN. Moreover, studies have also marked spatial differences in the default mode network in schizophrenia patients together with significant higher frequency fluctuations in default mode regions, as well as hyperactivity and hyperconnectivity of the default mode network in patients in the early phase of schizophrenia [64]. These studies suggest an important role for the default mode network in the pathophysiology of schizophrenia.

Altered functional connectivity patterns have been reported in other neurological disorders. For example, in **multiple sclerosis (MS)** a decreased functional connectivity in the primary motor has been established. This has substantiated studies that showed decreased microstructural integrity of the callosal white matter tracts [66]. Additionally, in patients with amyotrophic lateral sclerosis (ALS), ICA analysis has suggested a decrease in functional connectivity [67].

### **5. Global initiatives and the Human Connectome Project**

60 Novel Frontiers of Advanced Neuroimaging

conditions [4, 57].

diseases.

parietal cortical regions and medial temporal lobe [53].

has also been found to be negatively correlated with regions that tend to increase their activity during attention demanding tasks. Other identified networks include a self-referential system engaging the medial prefrontal regions; a posterior network involved in visual processing; an attention network engaging superior frontal and parietal cortex; a superior temporal system; and a network-engaging precentral and postcentral cortex [3, 7]. The most consistently reported resting state networks include the primary sensorimotor network; the primary visual and extra-striate visual network consisting of bilateral temporal/insular and anterior cingulate cortex regions; left and right lateralized networks consisting of superior parietal and superior frontal regions; and the default mode network consisting of precuneus, medial frontal, inferior

Given the success of resting state functional connectivity for probing the brain's functional architecture in normal subjects, it is a great benefit to employ this technique towards the investigation of dysfunction in the brain. A review by Fox and Greicius [8] highlights advantages of examining the resting state signal for clinical applications and discusses methodological issues that need to be resolved to facilitate translational applications of rsfMRI. A number of clinical applications are already emerging as emphasized by the studies of functional connectivity in premature children [54], adolescents with schizotypal traits [55], major depression, and aging [56]. Two recent reviews detail the large number of studies that have utilized resting state fcMRI to study various neurological and psychiatric

The functional connectivity of the DMN has been linked to core process of human cognition such as the integration of cognitive and emotional processing [7], monitoring [58] and mindwandering. As a result, analysis of the connectivity patterns of the DMN is especially important in examining cognitive dysfunction in neurological and neuropsychiatric brain disorders [57, 59]. DMN alterations have been reported in a number of neuropsychiatric

It has been shown that the DMN exhibits decreased connectivity patters in patients with **Alzheimer disease** [60]**,** decreased correlations within the DMN including hippocampi, decreased anticorrelations with the DMN, and reduced local connectivity as reflected in

**Schizophrenia** has been marked as a potential disconnection disease [61]. Widespread functional disconnectivity between brain regions has been suggested to underlie these symptoms [62, 63]. Schizophrenia is known to have aberrant effects on frontal and parietal regions involved with the DMN. Resting state fMRI studies have reported a decrease in the functional connectivity between medial frontal cortex and precuneus in schizophrenic patients [64]. Additionally, diffusion tensor imaging studies have reported diminished white matter integrity in patients with schizophrenia [65]. Specifically, a decrease in white matter integrity was observed in the cingulum tract, which is known to be interconnected with DMN regions MFC and PCC [50, 57]. Therefore, symptoms of patients with

clustering coefficients, in which parts of this network have been clearly implicated.

**4.3. Networks involved in neurological and psychiatric diseases**

There has been a long standing interest in the unrestricted sharing and access of functional neuroimaging data within the neuroimaging community. Inherent within the methodology of task based fMRI, the complexity of conforming task paradigms across different acquisition sites limited the potential of having such an open access platform. However, with the introduction of the unique methodological approach of resting state fMRI, a new era in open access data sharing was ushered. Most notably, a data sharing consortium - the 1000 Functional Connectomes project (fcon\_1000.projects.nitrc.org) - composed of various groups from all around began this initiative in 2009. The fcon1000 project aggregated and publicly released over 1200 resting state and anatomical MRI datasets acquired at 33 sites around the world. Within the first six months, the release generated over 9000 download from 78 countries. The benefit of unrestricted data sharing on the advancement of neuroimaging is self-evident. Researchers with the resources to acquire data can ensure that their resources are most widely used, while those researchers who prefer to focus on data analysis can do so. Such initiatives had wide implications on the neuroimaging community. Since large sets of data are now freely accessible, the valued commodity of the imaging community is no longer the data itself, but the analytic tools and approaches of interpretation and analysis. This new paradigm of unrestricted data access will be an integral part of the future of brain research. The human brain is usually divided into several hundred areas that exhibit highly specialized function. Cortical and subcortical regions of divergent function exhibit distinct cytoarchitecture when viewed under a microscope. As the human brain develops, mechanisms of axonal guidance allow the projection of millions of axonal fibbers to target destinations. In the process, a complex network of information processing pathways is formed.

The Human Connectome Project (HCP) [68] is an ambitious five year initiative that was launched in 2010. Its main objective is to build a network map of the human brain to shed light on its structural and functional connectivity. HCP leans on the general facet that the function of a system is determined by its structure. By elucidating the complete underlying

structure of white matter pathways, we will be able to glean significant information about the function of this composite organ.

Brain Connections – Resting State fMRI Functional Connectivity 63

and Innovation (ISCIII: FIS P.I. 02/0018, P.I. 05/2332.), Spanish Mental Health Network:

[1] Kwong KK, Belliveau JW, Chesler DA, Goldberg IE, Weisskoff RM, Poncelet BP, et al. (1992) Dynamic magnetic resonance Imaging of human brain activity during primary

[2] Logothetis NK (2008) What we can do and what we cannot do with fMRI.  Nat Rev Neurol. 6: 15-

[5] Biswal B, Yetkin FZ, Haughton VM, Hyde JS (1995) Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med. 34: 537-

[6] Fair DA, Dosenbach NU, Church JA, Cohen AL, Brahmbhatt S, Miezin FM, et al. (2007) Development of distinct control networks through segregation and integration. Proc

[7] Greicius M (2008) Resting-state functional connectivity in neuropsychiatric disorders.

[8] Fox MD, Greicius M (2010) Clinical applications of resting state functional connectivity.

[9] Walters NB, Eickhoff SB, Schleicher A, Zilles K, Amunts K, Egan G, et al. (2007) Observer-Independent Analysis of High-Resolution MR Images of the Human Cerebral

[11] Stejskal EO, Tanner JE (1965) Spin Diffusion Measurements: Spin Echoes in the Presence of a Time-Dependent Field Gradient. The Journal of Chemical Physics 42: 288-292. [12] Le Bihan D, Breton E, Lallemand D, Grenier P, Cabanis E, Laval-Jeantet M (1986) MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in

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[14] Basser PJ, Pajevic S, Pierpaoli C, Duda J, Aldroubi A (2000) In vivo fiber tractography

[15] De la Iglesia-Vaya M, Molina-Mateo J, Escarti-Fabra MJ, Martí-Bonmatí L, Robles M, Meneu T, et al. (2011) Magnetic resonance imaging postprocessing techniques in the

[16] Ellison-Wright I, Bullmore E (2009) Meta-analysis of diffusion tensor imaging studies in

[17] Lawes IN, Barrica TR, Murugam V, Clark CA (2007) Atlas based segmentation of white matter tracts of the human brain using diffusion tensor tractography and comparison

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[10] Hahn EL. (1950) Phys. Rev. 80: 580-594.

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CIBERSAM.

**6. References**

869-878.

28.

541.

The term *Connectome* was introduced by Olaf Sporns in analogy to the human genome. An approach that is more elaborate than mapping Connectomes, is mapping Synaptomes of the brain. Such an effort would require imaging synaptic clefts at ultra-structural levels of resolution. This endeavour is currently out of reach of the methods we possess today, as it necessitates acquiring images of proteins and neurochemicals that govern the various biochemical pathways of all synapses. Nonetheless, by relying on simple models systems such as *Caenorhabditis elegans* – which has a total number of 302 neurons – we can examine the feasibility of such a project.

Such mapping undertakings would provide considerable information to solve mysteries of brain function. Connectivity in the brain can be studies at three levels of analysis. Combining information from macroscopic, microscopic and nanoscopic levels of analysis, would have wide implications on appreciating the full structural map of the brain.

### **Author details**

Maria de la Iglesia-Vaya *Centre of Excelence in Biomedical Image (CEIB-CIPF), Medical Bioimage Unit, Bioinformatics & Genomics Department. Prince Felipe Research Centre (CIPF). Eduardo Primo Yúfera (Científic), Valencia,Spain CIBERSAM, ISC III, Valencia, Spain*

Jose Molina-Mateo *Centre for Biomaterials and Tissue Engineering, Universitat Politècnica de València, (Spain)*

Jose Escarti-Fabra *CIBERSAM, ISC III, Valencia, Spain Psychiatric Unit, Clinic Hospital, Valencia, Spain*

Ahmad S. Kanaan *Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig,Germany*

Luis Martí-Bonmatí *Centre of Excelence in Biomedical Image (CEIB-CIPF), Medical Bioimage Unit, Bioinformatics & Genomics Department. Prince Felipe Research Centre (CIPF). Eduardo Primo Yúfera (Científic), Valencia,Spain Radiology. Faculty of Medicine, Universitat de Valencia, Spain*

### **Acknowledgement**

The authors wish to thank Erika Proal, Xavier Castellanosand Daniel Margulies for their helpful discussion. This study was supported by Spanish grants from Ministry of Science and Innovation (ISCIII: FIS P.I. 02/0018, P.I. 05/2332.), Spanish Mental Health Network: CIBERSAM.

#### **6. References**

62 Novel Frontiers of Advanced Neuroimaging

the feasibility of such a project.

**Author details**

Jose Molina-Mateo

Jose Escarti-Fabra

Ahmad S. Kanaan

Luis Martí-Bonmatí

**Acknowledgement**

Maria de la Iglesia-Vaya

*Eduardo Primo Yúfera (Científic), Valencia,Spain*

*Centre for Biomaterials and Tissue Engineering, Universitat Politècnica de València, (Spain)*

*Psychiatric Unit, Clinic Hospital, Valencia, Spain*

*Eduardo Primo Yúfera (Científic), Valencia,Spain*

*Radiology. Faculty of Medicine, Universitat de Valencia, Spain*

*CIBERSAM, ISC III, Valencia, Spain*

*CIBERSAM, ISC III, Valencia, Spain*

the function of this composite organ.

structure of white matter pathways, we will be able to glean significant information about

The term *Connectome* was introduced by Olaf Sporns in analogy to the human genome. An approach that is more elaborate than mapping Connectomes, is mapping Synaptomes of the brain. Such an effort would require imaging synaptic clefts at ultra-structural levels of resolution. This endeavour is currently out of reach of the methods we possess today, as it necessitates acquiring images of proteins and neurochemicals that govern the various biochemical pathways of all synapses. Nonetheless, by relying on simple models systems such as *Caenorhabditis elegans* – which has a total number of 302 neurons – we can examine

Such mapping undertakings would provide considerable information to solve mysteries of brain function. Connectivity in the brain can be studies at three levels of analysis. Combining information from macroscopic, microscopic and nanoscopic levels of analysis,

would have wide implications on appreciating the full structural map of the brain.

*Centre of Excelence in Biomedical Image (CEIB-CIPF), Medical Bioimage Unit, Bioinformatics & Genomics Department. Prince Felipe Research Centre (CIPF).*

*Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig,Germany*

*Centre of Excelence in Biomedical Image (CEIB-CIPF), Medical Bioimage Unit, Bioinformatics & Genomics Department. Prince Felipe Research Centre (CIPF).*

The authors wish to thank Erika Proal, Xavier Castellanosand Daniel Margulies for their helpful discussion. This study was supported by Spanish grants from Ministry of Science

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[38] Liu J, Demirci O, Calhoun VD (2008) A Parallel Independent Component Analysis Approach to Investigate Genomic Influence on Brain Function. IEEE Signal Proc.

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**Chapter 4**

© 2013 Kato and Izumiyama, licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2013 Kato and Izumiyama, licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

distribution, and reproduction in any medium, provided the original work is properly cited.

**Activation of Brain Sensorimotor Network by**

**Hemiparetic Stroke: A Functional MRI Study**

Stroke is one of the leading causes of disability in the elderly in many countries. Residual motor impairment, especially hemiparesis, is one of the most common sequelae after stroke. Motor recovery after stroke exhibits a wide range of difference among patients, and is dependent on the location and amount of brain damage, degree of impairment, and nature of deficit (Duncan et al., 1992). Full recovery of motor function is often observed when initial impairment is mild, but recovery is limited when there were severe deficits at stroke onset. The motor recovery after stroke may be caused by the effects of medical therapy against acute stroke, producing a resolution of brain edema and an increase in cerebral blood flow in the penumbra and remote areas displaying diaschisis. However, functional improvements may be seen past the period of acute tissue response and its resolution. The role of rehabilitation in facilitating motor recovery is considered to be produced by

Non-invasive neuroimaging techniques, including functional magnetic resonance imaging (fMRI) and positron emission tomography (PET), enable us to measure task-related brain activity with excellent spatial resolution (Herholz & Heiss, 2000; Calautti & Baron, 2003; Rossini et al., 2003). The functional neuroimaging studies usually employ active motor tasks, such as hand grip and finger tapping, and require that the patients are able to move their hand. Neuroimaging studies in stroke patients have reported considerable amounts of data that suggest the mechanisms of motor functional recovery after stroke. Initial cross-sectional studies at chronic stages of stroke have demonstrated that the pattern of brain activation is different between paretic and normal hand movements, and suggested that long-term recovery is facilitated by compensation, recruitment and reorganization of cortical motor

**Somatosensory Input in Patients with**

Hiroyuki Kato and Masahiro Izumiyama

http://dx.doi.org/10.5772/51693

promoting brain plasticity.

**1. Introduction**

Additional information is available at the end of the chapter

## **Activation of Brain Sensorimotor Network by Somatosensory Input in Patients with Hemiparetic Stroke: A Functional MRI Study**

Hiroyuki Kato and Masahiro Izumiyama

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/51693

### **1. Introduction**

66 Novel Frontiers of Advanced Neuroimaging

Curr Opin Neurol. 21: 424-430.

Proc Natl Acad Sci USA 101: 4637-4642.

mechanisms. Biol. Psychiatry. 46: 908–920.

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of the human brain. PLoS Comput Biol. 1: 245-251.

Acad Sci USA. 106: 1279-1284.

89–97.

resting human brain. Nat Rev Neurosci. 2: 685-694.

1961. New York: International Universities Press.

structural and functional systems. Nat Rev Neurosci. 10: 186-198.

 Eur Neuropsychopharmacol. 20: 519-534. [54] Damaraju E, Phillips JR, Lowe JR, Ohls R, Calhoun VD, Caprihan A (2010) Resting-state functional connectivity differences in premature children. Front Syst Neurosci. 4: 23. [55] Lagioia A, Eliez S, Schneider M, Simons JS, Van der Linden M, Debbané M. (2011) Neural correlates of reality monitoring during adolescence. Neuroimage. 55: 1393-1400. [56] Langan J, Peltier SJ, Bo J, Fling BW, Welsh RC, Seidler RD (2010) Functional implications of age differences in motor system connectivity. Front Syst Neurosci. 4: 17. [57] Greicius M (2008) Resting-state functional connectivity in neuropsychiatric disorders.

[58] Gusnard DA, Raichle ME (2001) Searching for a baseline: functional imaging and the

[59] Bullmore E, Sporns O (2009) Complex brain networks: graph theoretical analysis of

[60] Greicius M, Srivastava G, Reiss A, Menon V. (2004) Default-mode network activity distinguishes Alzheimer's disease from healthy aging: Evidence from functional MRI.

[61] Bleuler E (1911) Dementia Praecox or the Group of Schizophrenias. English Translation

[62] Andreasen NC, Nopoulos P, O'Leary DS, Miller DD, Wassink T, Flaum M (1999) Defining the phenotype of schizophrenia: cognitive dysmetria and its neural

[63] Friston KJ, Frith CD (1995) Schizophrenia: a disconnection syndrome? Clin. Neurosci. 3:

[64] Whitfield-Gabrieli S, Thermenos HW, Milanovic S, Tsuang MT, Faraone SV, McCarley RW, et al. (2009) Hyperactivity and hyperconnectivity of the default network in schizophrenia and in first-degree relatives of persons with schizophrenia. Proc Natl

[65] Ellison-Wright I, Bullmore E (2009) Meta-analysis of diffusion tensor imaging studies in

[66] Lowe MJ, Beall EB, Sakaie KE, Koenig KA, Stone L, Marrie RA, et al. (2008) Resting state sensorimotor functional connectivity in multiple sclerosis inversely correlates with transcallosal motor pathway transverse diffusivity. Hum Brain Mapp. 29: 818-827. [67] Mohammadi B, Kollewe K, Samii A, Krampfl K, Dengler R, Münte TF (2009) Changes of resting state brain networks in amyotrophic lateral sclerosis. Exp Neurol. 217: 147-153. [68] Sporns O, Tononi G, Kötter R. (2005) The human connectome: A structural description

default mode of brain function. Proc Natl Acad Sci USA. 98: 676-682.

Stroke is one of the leading causes of disability in the elderly in many countries. Residual motor impairment, especially hemiparesis, is one of the most common sequelae after stroke. Motor recovery after stroke exhibits a wide range of difference among patients, and is dependent on the location and amount of brain damage, degree of impairment, and nature of deficit (Duncan et al., 1992). Full recovery of motor function is often observed when initial impairment is mild, but recovery is limited when there were severe deficits at stroke onset. The motor recovery after stroke may be caused by the effects of medical therapy against acute stroke, producing a resolution of brain edema and an increase in cerebral blood flow in the penumbra and remote areas displaying diaschisis. However, functional improvements may be seen past the period of acute tissue response and its resolution. The role of rehabilitation in facilitating motor recovery is considered to be produced by promoting brain plasticity.

Non-invasive neuroimaging techniques, including functional magnetic resonance imaging (fMRI) and positron emission tomography (PET), enable us to measure task-related brain activity with excellent spatial resolution (Herholz & Heiss, 2000; Calautti & Baron, 2003; Rossini et al., 2003). The functional neuroimaging studies usually employ active motor tasks, such as hand grip and finger tapping, and require that the patients are able to move their hand. Neuroimaging studies in stroke patients have reported considerable amounts of data that suggest the mechanisms of motor functional recovery after stroke. Initial cross-sectional studies at chronic stages of stroke have demonstrated that the pattern of brain activation is different between paretic and normal hand movements, and suggested that long-term recovery is facilitated by compensation, recruitment and reorganization of cortical motor

© 2013 Kato and Izumiyama, licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2013 Kato and Izumiyama, licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

function in both damaged and non-damaged hemispheres (Chollet et al., 1991; Weiller et al., 1992; Cramer et al., 1997; Cao et al., 1998; Ward et al., 2003a). Subsequent longitudinal studies from subacute to chronic stages (before and after rehabilitation) have revealed a dynamic, bihemispheric reorganization of motor network, and emphasized the necessity of successive studies (Marshall et al., 2000; Calautti et al., 2001; Feydy et al., 2002; Ward et al, 2003b).

 Patient characteristics

**2.2. Functional MRI**

Passive movement

H Stroke location

radiata

capsule

radiata

capsule

cortex

Blt = bilateral; SPC = superior parietal cortex; IPC = inferior parietal cortex;

the start and stop signals of hand grip by tapping gently on the knee.

6 76M L R pons DM

HT DM

HT DM HL

HT HL

HT HL af

HT

M = male; F = female; H = hemiparesis; R = right; L = left; MCA = middle cerebral artery; PMH = past medical history; HT = hypertension; DM = diabetes mellitus; HL = hyperlipidemia;af = atrial fibrillation; N = non-affected hand, P = paretic hand; S1M1 = primary sensorimotor cortex; SMA = supplementary motor areas: Cbll = cerebellum;

The fMRI studies were performed using a 1.5 T Siemens Magnetom Symphony MRI scanner as described previously (Kato et al., 2002).  The acquisition parameters were as follows: repetition time 3 s, time of echo 50 ms, flip angle 90°, 3-mm slice thickness, 30 slices through the entire brain, field of view 192 x 192 mm, and 128 x 128 matrix. During the fMRI scan, the patients and normal controls received or performed a task as mentioned above. This task performance occurred in periods of 30 s, interspaced with 30 s rest periods. The cycle of rest and task was repeated 5 times during each hand study. Therefore, the fMRI scan of each hand study took 5 min to complete, producing 3,000 images. A staff member monitored the patient directly throughout the study, and gave the sensory stimulations or

When the stroke patients are unable to move their hand, alternative paradigms are necessary to study their brain function. Passive, instead of active, hand movement has been employed for this purpose, and increases in brain activities are found not only in sensory but also motor cortices (Nelles et al., 1999; Loubinoux et al., 2003; Tombari et al., 2004). Functional neuroimaging studies suggest that a change in processing of somatosensory information in the sensorimotor cortex may play an important role in motor recovery after stroke (Schaechter et al., 2006).

Most significant recovery of motor function takes place within the first weeks after stroke and an early introduction of rehabilitation is crucial for a good outcome. Rehabilitation at the early stages of stroke uses physiotherapy, such as massage and passive movement of the paretic hand, as an initial step of rehabilitation, especially in patients with severe motor impairment. However, it is difficult to assess the effects of physiotherapy in patients with severe impairment early after stroke. In this fMRI study, we investigated the effects of somatosensory input on the activity of brain sensorimotor network in stroke patients. Since somatosensory feedback is essential for the exact execution of hand movement, the result can provide a scientific basis for the establishment of rehabilitation strategies.

### **2. Materials and methods**

#### **2.1. Subjects**

We selected 6 stroke patients with pure motor hemiparesis (4 men and 2 women, 63-85 years old). Three of them received fMRI during a task of unilateral palm brushing (stimulation of tactile sensation using a plastic hairbrush at approximately 1 Hz), and three other patients received fMRI during a task of unilateral passive hand movement (stimulation of proprioceptive sensation by passive flexion-extension of fingers at approximately 1 Hz). The fMRI studies were performed 5 days to 2 months after stroke onset.

The patients presented with neurological deficits including moderate to severe hemiparesis, and were admitted to our hospital. They received standard medical therapy for stroke and rehabilitation. All of them were right-handed. All the cerebral infarcts were evidenced by MRI, and were located in various regions of the cerebrum. They could hardly move their hands when the fMRI was performed. Clinical data are summarized in Table 1. Three righthanded, normal subjects (59-68 years of age; 2 men and 1 woman) served as controls for a comparison to show normal brain activation during a unilateral hand grip task. This study was approved by the ethics committee of our hospital and informed consent was obtained from all subjects in accordance with the Declaration of Helsinki.

Activation of Brain Sensorimotor Network by

Somatosensory Input in Patients with Hemiparetic Stroke: A Functional MRI Study 69

M = male; F = female; H = hemiparesis; R = right; L = left; MCA = middle cerebral artery; PMH = past medical history; HT = hypertension; DM = diabetes mellitus; HL = hyperlipidemia;af = atrial fibrillation; N = non-affected hand, P = paretic hand; S1M1 = primary sensorimotor cortex; SMA = supplementary motor areas: Cbll = cerebellum; Blt = bilateral; SPC = superior parietal cortex; IPC = inferior parietal cortex;

**Table 1.** Patient characteristics

#### **2.2. Functional MRI**

68 Novel Frontiers of Advanced Neuroimaging

stroke (Schaechter et al., 2006).

**2. Materials and methods**

**2.1. Subjects**

2003b).

function in both damaged and non-damaged hemispheres (Chollet et al., 1991; Weiller et al., 1992; Cramer et al., 1997; Cao et al., 1998; Ward et al., 2003a). Subsequent longitudinal studies from subacute to chronic stages (before and after rehabilitation) have revealed a dynamic, bihemispheric reorganization of motor network, and emphasized the necessity of successive studies (Marshall et al., 2000; Calautti et al., 2001; Feydy et al., 2002; Ward et al,

When the stroke patients are unable to move their hand, alternative paradigms are necessary to study their brain function. Passive, instead of active, hand movement has been employed for this purpose, and increases in brain activities are found not only in sensory but also motor cortices (Nelles et al., 1999; Loubinoux et al., 2003; Tombari et al., 2004). Functional neuroimaging studies suggest that a change in processing of somatosensory information in the sensorimotor cortex may play an important role in motor recovery after

Most significant recovery of motor function takes place within the first weeks after stroke and an early introduction of rehabilitation is crucial for a good outcome. Rehabilitation at the early stages of stroke uses physiotherapy, such as massage and passive movement of the paretic hand, as an initial step of rehabilitation, especially in patients with severe motor impairment. However, it is difficult to assess the effects of physiotherapy in patients with severe impairment early after stroke. In this fMRI study, we investigated the effects of somatosensory input on the activity of brain sensorimotor network in stroke patients. Since somatosensory feedback is essential for the exact execution of hand movement, the result

We selected 6 stroke patients with pure motor hemiparesis (4 men and 2 women, 63-85 years old). Three of them received fMRI during a task of unilateral palm brushing (stimulation of tactile sensation using a plastic hairbrush at approximately 1 Hz), and three other patients received fMRI during a task of unilateral passive hand movement (stimulation of proprioceptive sensation by passive flexion-extension of fingers at approximately 1 Hz). The

The patients presented with neurological deficits including moderate to severe hemiparesis, and were admitted to our hospital. They received standard medical therapy for stroke and rehabilitation. All of them were right-handed. All the cerebral infarcts were evidenced by MRI, and were located in various regions of the cerebrum. They could hardly move their hands when the fMRI was performed. Clinical data are summarized in Table 1. Three righthanded, normal subjects (59-68 years of age; 2 men and 1 woman) served as controls for a comparison to show normal brain activation during a unilateral hand grip task. This study was approved by the ethics committee of our hospital and informed consent was obtained

can provide a scientific basis for the establishment of rehabilitation strategies.

fMRI studies were performed 5 days to 2 months after stroke onset.

from all subjects in accordance with the Declaration of Helsinki.

The fMRI studies were performed using a 1.5 T Siemens Magnetom Symphony MRI scanner as described previously (Kato et al., 2002).  The acquisition parameters were as follows: repetition time 3 s, time of echo 50 ms, flip angle 90°, 3-mm slice thickness, 30 slices through the entire brain, field of view 192 x 192 mm, and 128 x 128 matrix. During the fMRI scan, the patients and normal controls received or performed a task as mentioned above. This task performance occurred in periods of 30 s, interspaced with 30 s rest periods. The cycle of rest and task was repeated 5 times during each hand study. Therefore, the fMRI scan of each hand study took 5 min to complete, producing 3,000 images. A staff member monitored the patient directly throughout the study, and gave the sensory stimulations or the start and stop signals of hand grip by tapping gently on the knee.

Data analysis was performed using Statistical Parametric Mapping (SPM) 2 (Wellcome Department of Cognitive Neurology, London, UK, http://www.fil.ion.ucl.ac.uk/spm/) implemented in MATLAB (The MathWorks Inc., Natick, MA, USA). After realignment and smoothing, the general linear model was employed for the detection of activated voxels. The voxels were considered as significantly activated if p<0.05 using the FWE analysis. All the measurements were performed with this same statistical threshold. The activation images were overlaid on corresponding T1-weighted anatomic images.

Activation of Brain Sensorimotor Network by

Somatosensory Input in Patients with Hemiparetic Stroke: A Functional MRI Study 71

**Figure 2.** fMRI of a 79-year old man (patient 5) who had a cerebral infarct in part of the right middle cerebral artery territory (arrow in a, diffusion-weighted MRI). After 13 days of stroke onset, passive movement of the left (unaffected) hand (e-g) induced activation in the right primary sensorimotor cortex (1) and left cerebellum (3). During passive movement of the right (paretic) hand (b-d), activation

**Figure 3.** fMRI of a 61-year old man (control). Active right hand movement (a-c) induced a normal activation pattern in the left primary sensorimotor cortex (1), supplementary motor areas (2) and right

The results demonstrated that somatosensory stimulation of the unaffected hand, both tactile and proprioceptive input, activated sensorimotor network in the brain, and that the activation pattern was similar to that induced by active hand movement. Somatosensory input to the paretic hand also activated the sensorimotor network in the brain, although to a

**4.1. Activation of sensorimotor network by somatosensory input**

in contralateral primary sensorimotor cortex (1) was observed.

cerebellum (3).

**4. Discussion**

### **3. Results**

Both tactile and proprioceptive inputs via the unaffected hand activated contralateral primary sensorimotor cortex (S1M1) in all the patients, and the supplementary motor areas (SMA) and the ipsilateral cerebellum in part of the patients (Table, Figs. 1 &2). This activation pattern is similar to that activated during active hand movement (Fig. 3), although the activation was less extensive. Both tactile and proprioceptive inputs via the paretic hand also activated the contralateral S1M1 in all the patients, and in SMA and superior and inferior parietal cortices in part of the patients (Table, Figs. 1 & 2), although to a lesser extent as compared with unaffected hand. No cerebellar activation was observed when paretic hand was stimulated.

**Figure 1.** fMRI of a 68-year old man (patient 1) who had a cerebral infarct in the right corona radiata (arrow in a, diffusion-weighted MRI) . After 28 days of stroke onset, palm brushing of the right (unaffected) hand (b-d) induced activation in the left primary sensorimotor cortex (1), the supplementary motor area (2), and right cerebellum (3). During palm brushing of the left (paretic) hand (e-g), activation in contralateral primary sensorimotor cortex (1) was seen, although less extensive, and no activation was seen in the supplementary motor areas and the cerebellum.

Activation of Brain Sensorimotor Network by Somatosensory Input in Patients with Hemiparetic Stroke: A Functional MRI Study 71

**Figure 2.** fMRI of a 79-year old man (patient 5) who had a cerebral infarct in part of the right middle cerebral artery territory (arrow in a, diffusion-weighted MRI). After 13 days of stroke onset, passive movement of the left (unaffected) hand (e-g) induced activation in the right primary sensorimotor cortex (1) and left cerebellum (3). During passive movement of the right (paretic) hand (b-d), activation in contralateral primary sensorimotor cortex (1) was observed.

**Figure 3.** fMRI of a 61-year old man (control). Active right hand movement (a-c) induced a normal activation pattern in the left primary sensorimotor cortex (1), supplementary motor areas (2) and right cerebellum (3).

### **4. Discussion**

70 Novel Frontiers of Advanced Neuroimaging

when paretic hand was stimulated.

**3. Results**

Data analysis was performed using Statistical Parametric Mapping (SPM) 2 (Wellcome Department of Cognitive Neurology, London, UK, http://www.fil.ion.ucl.ac.uk/spm/) implemented in MATLAB (The MathWorks Inc., Natick, MA, USA). After realignment and smoothing, the general linear model was employed for the detection of activated voxels. The voxels were considered as significantly activated if p<0.05 using the FWE analysis. All the measurements were performed with this same statistical threshold. The activation images

Both tactile and proprioceptive inputs via the unaffected hand activated contralateral primary sensorimotor cortex (S1M1) in all the patients, and the supplementary motor areas (SMA) and the ipsilateral cerebellum in part of the patients (Table, Figs. 1 &2). This activation pattern is similar to that activated during active hand movement (Fig. 3), although the activation was less extensive. Both tactile and proprioceptive inputs via the paretic hand also activated the contralateral S1M1 in all the patients, and in SMA and superior and inferior parietal cortices in part of the patients (Table, Figs. 1 & 2), although to a lesser extent as compared with unaffected hand. No cerebellar activation was observed

**Figure 1.** fMRI of a 68-year old man (patient 1) who had a cerebral infarct in the right corona radiata (arrow in a, diffusion-weighted MRI) . After 28 days of stroke onset, palm brushing of the right (unaffected) hand (b-d) induced activation in the left primary sensorimotor cortex (1), the

supplementary motor area (2), and right cerebellum (3). During palm brushing of the left (paretic) hand (e-g), activation in contralateral primary sensorimotor cortex (1) was seen, although less extensive, and

no activation was seen in the supplementary motor areas and the cerebellum.

were overlaid on corresponding T1-weighted anatomic images.

#### **4.1. Activation of sensorimotor network by somatosensory input**

The results demonstrated that somatosensory stimulation of the unaffected hand, both tactile and proprioceptive input, activated sensorimotor network in the brain, and that the activation pattern was similar to that induced by active hand movement. Somatosensory input to the paretic hand also activated the sensorimotor network in the brain, although to a

lesser degree. Of importance was that the activation involved not only postcentral S1 but also precentral M1, as observed in previous reports employing somatosensory stimulation as a task.

Activation of Brain Sensorimotor Network by

Somatosensory Input in Patients with Hemiparetic Stroke: A Functional MRI Study 73

enlargements of the hand representation in the remote PMv that are proportional to the amount of hand representation destroyed in M1 (Frost et al., 2003; Dancause et al., 2005). Nelles et al. (2001) pointed out the crucial role of a network including the lower part of BA40 and PMv, bilaterally, in task-oriented passive training aimed at improving motor recovery in severely impaired stroke patients. These areas could also be crucial for promoting

Previous functional neuroimaging studies on poststroke cerebral reorganization from acute to chronic stages revealed several activation patterns during active paretic hand movement (Ward & Cohen, 2004; Jang, 2007; Kato & Izumiyama, 2010). These include (1) a posterior shift of contralateral S1M1 activation (Pineiro et al., 2001; Calautti et al., 2003) , (2) periinfarct reorganization after infarction involving M1 (Cramer et al., 1997; Jang et al., 2005a), (3) a shift of M1 activation to the ipsilateral (contralesional) cortex (Chollet et al., 1991; Marshall et al., 2000; Feydy et al., 2002), (4) contribution of the secondary motor areas (Cramer et al., 1997; Carey et al., 2002; Ward et al., 2006), and (5) higher contralateral activity

These studies have also shown that the expanded activations may later decrease with functional improvements, indicating that best recovery is obtained when there is restitution of activation toward the physiological network over time. The contralesional shift of activation may return to ipsilesional S1M1 activation with functional gains (Feydy et al., 2002; Takeda et al., 2007), but worse outcome may correlate with a shift in the balance of activation toward the contralesional S1M1 (Calautti et al., 2001; Feydy et al., 2002; Zemke et al., 2003). Thus, the patterns of cerebral activation evoked by active hand movement show impaired organization and reorganization of brain sensorimotor network, and best recovery may depend on how much original motor system is reusable. The patterns of activation may also be dependent on the patient's ability to recruit residual portions of the bilateral motor

Early involvement of secondary sensorimotor areas after M1 lesion may temporarily substitute for the original sensorimotor network involving M1. This step may be a prerequisite to M1 functional reconnection through indirect pathways and to its efficacy in processing motor signals. The previous data suggest that different motor areas operate in parallel rather than in a hierarchical manner, and they are able to substitute for each other (Traversa et al., 1997; Loubinoux et al., 2003). Thus, remodeling of activation within a pre-

There is consensus on the efficacy of physiotherapy. Active training is more efficient than passive training, but active training cannot be applied to very impaired patients. We need to consider other approaches for patients who cannot move the paretic limbs at the early phase of recovery. Physiotherapists apply sensory stimulation and passive movement daily to

**4.2. Activation of sensorimotor network by active motor task**

reorganization in the rest of the brain.

in the cerebellar hemisphere (Small et al., 2002).

existing network may be an important process for recovery.

**4.3. Implication of somatosensory input as a rehabilitation strategy**

network (Silvestrini et al., 1998).

Passive movement studies have shown that brain activation during passive movement is seen in regions such as the contralateral sensorimotor cortex, the bilateral premotor cortex, supplementary motor areas, and inferior parietal cortex (Nelles et al., 1999; Loubinoux et al., 2003; Tombari et al., 2004). The similarity of activation patterns between passive and active hand movements highlights the contribution of afferent synaptic activity for central motor control, and suggests that the sensory systems play an important role in central motor control. Additional explanation may be that the repetitive sensory input induces motor imagery in the patients. Imagery of movement activates largely the same brain areas that are activated when movements are actually executed (Decety, 1996; Grezes & Decety 2001).

The brain activation during paretic hand sensory stimulation in this study was reduced as compared to that during unaffected hand sensory stimulation. This reduction may reflect the sensorimotor network damage caused by stroke, although the fMRI BOLD response could be reduced in the cerebral hemisphere of the lesion side (Murata et al., 2006; Mazzetto-Betti et al., 2010). Nevertheless, the result confirms the possibility of inducing sensorimotor transformations even in severely impaired stroke patients.

The observation of S1 and M1 activation during sensory input as well as active movement suggests that the sensorimotor network is functionally connected with each other. Actually, human motor and sensory hand cortices overlap, and are not divided in a simple manner by the central sulcus (McGlone et al., 2002; Morre et al.; 2000; Nii et al., 1996). Furthermore, S1 and M1 are heavily interconnected (Jones et al., 1978) and both are the sites of origin of pyramidal tract neurons in the monkey (Fromm & Evarts, 1982). Proprioceptive afferents from the muscle spindles (fibers IA, II), along with the projections from other articular and cutaneous receptors (fibers I to IV), gain access not only to S1 but also to M1 in the monkey (Lemon, 1999; Lemon & Porter, 1976).

Previous studies have also demonstrated the activation of secondary sensorimotor areas induced by passive hand movements, as seen in our study. SMA has rich anatomical connections with many areas in the central nervous system, such as thalamus, dorsal premotor cortex (PMd), spinal cord, and contralateral hemisphere (Juergens, 1984; Rouiller et al., 1994; Dum & Strick, 1996; Dum & Strick, 2005), and may be an important source of descending commands for the generation and control of distal movements in the monkey (He et al., 1995). SMA is also involved in motor learning in man (Halsband & Lange, 2006). Therefore, SMA has been suggested to play a crucial role in the early processes of recovery after lesions of primary motor pathways (Loubinoux et al., 2003).

Ventral premotor cortex (PMv) receives strong projections from S1 (Stepniewska et al., 2006), and PMv neurons project onto cervical and thoracic motoneurons in the monkey (He et al., 1993; Rouiller et al., 1994). The PMv corticospinal neurons supply part of the hand function after M1 lesion in the monkey (Liu & Rouiller, 1999). Nudo and colleagues demonstrated rewiring from M1 to PMv after ischemic brain injury, with substantial enlargements of the hand representation in the remote PMv that are proportional to the amount of hand representation destroyed in M1 (Frost et al., 2003; Dancause et al., 2005). Nelles et al. (2001) pointed out the crucial role of a network including the lower part of BA40 and PMv, bilaterally, in task-oriented passive training aimed at improving motor recovery in severely impaired stroke patients. These areas could also be crucial for promoting reorganization in the rest of the brain.

#### **4.2. Activation of sensorimotor network by active motor task**

72 Novel Frontiers of Advanced Neuroimaging

(Lemon, 1999; Lemon & Porter, 1976).

as a task.

lesser degree. Of importance was that the activation involved not only postcentral S1 but also precentral M1, as observed in previous reports employing somatosensory stimulation

Passive movement studies have shown that brain activation during passive movement is seen in regions such as the contralateral sensorimotor cortex, the bilateral premotor cortex, supplementary motor areas, and inferior parietal cortex (Nelles et al., 1999; Loubinoux et al., 2003; Tombari et al., 2004). The similarity of activation patterns between passive and active hand movements highlights the contribution of afferent synaptic activity for central motor control, and suggests that the sensory systems play an important role in central motor control. Additional explanation may be that the repetitive sensory input induces motor imagery in the patients. Imagery of movement activates largely the same brain areas that are activated when movements are actually executed (Decety, 1996; Grezes & Decety 2001).

The brain activation during paretic hand sensory stimulation in this study was reduced as compared to that during unaffected hand sensory stimulation. This reduction may reflect the sensorimotor network damage caused by stroke, although the fMRI BOLD response could be reduced in the cerebral hemisphere of the lesion side (Murata et al., 2006; Mazzetto-Betti et al., 2010). Nevertheless, the result confirms the possibility of inducing

The observation of S1 and M1 activation during sensory input as well as active movement suggests that the sensorimotor network is functionally connected with each other. Actually, human motor and sensory hand cortices overlap, and are not divided in a simple manner by the central sulcus (McGlone et al., 2002; Morre et al.; 2000; Nii et al., 1996). Furthermore, S1 and M1 are heavily interconnected (Jones et al., 1978) and both are the sites of origin of pyramidal tract neurons in the monkey (Fromm & Evarts, 1982). Proprioceptive afferents from the muscle spindles (fibers IA, II), along with the projections from other articular and cutaneous receptors (fibers I to IV), gain access not only to S1 but also to M1 in the monkey

Previous studies have also demonstrated the activation of secondary sensorimotor areas induced by passive hand movements, as seen in our study. SMA has rich anatomical connections with many areas in the central nervous system, such as thalamus, dorsal premotor cortex (PMd), spinal cord, and contralateral hemisphere (Juergens, 1984; Rouiller et al., 1994; Dum & Strick, 1996; Dum & Strick, 2005), and may be an important source of descending commands for the generation and control of distal movements in the monkey (He et al., 1995). SMA is also involved in motor learning in man (Halsband & Lange, 2006). Therefore, SMA has been suggested to play a crucial role in the early processes of recovery

Ventral premotor cortex (PMv) receives strong projections from S1 (Stepniewska et al., 2006), and PMv neurons project onto cervical and thoracic motoneurons in the monkey (He et al., 1993; Rouiller et al., 1994). The PMv corticospinal neurons supply part of the hand function after M1 lesion in the monkey (Liu & Rouiller, 1999). Nudo and colleagues demonstrated rewiring from M1 to PMv after ischemic brain injury, with substantial

sensorimotor transformations even in severely impaired stroke patients.

after lesions of primary motor pathways (Loubinoux et al., 2003).

Previous functional neuroimaging studies on poststroke cerebral reorganization from acute to chronic stages revealed several activation patterns during active paretic hand movement (Ward & Cohen, 2004; Jang, 2007; Kato & Izumiyama, 2010). These include (1) a posterior shift of contralateral S1M1 activation (Pineiro et al., 2001; Calautti et al., 2003) , (2) periinfarct reorganization after infarction involving M1 (Cramer et al., 1997; Jang et al., 2005a), (3) a shift of M1 activation to the ipsilateral (contralesional) cortex (Chollet et al., 1991; Marshall et al., 2000; Feydy et al., 2002), (4) contribution of the secondary motor areas (Cramer et al., 1997; Carey et al., 2002; Ward et al., 2006), and (5) higher contralateral activity in the cerebellar hemisphere (Small et al., 2002).

These studies have also shown that the expanded activations may later decrease with functional improvements, indicating that best recovery is obtained when there is restitution of activation toward the physiological network over time. The contralesional shift of activation may return to ipsilesional S1M1 activation with functional gains (Feydy et al., 2002; Takeda et al., 2007), but worse outcome may correlate with a shift in the balance of activation toward the contralesional S1M1 (Calautti et al., 2001; Feydy et al., 2002; Zemke et al., 2003). Thus, the patterns of cerebral activation evoked by active hand movement show impaired organization and reorganization of brain sensorimotor network, and best recovery may depend on how much original motor system is reusable. The patterns of activation may also be dependent on the patient's ability to recruit residual portions of the bilateral motor network (Silvestrini et al., 1998).

Early involvement of secondary sensorimotor areas after M1 lesion may temporarily substitute for the original sensorimotor network involving M1. This step may be a prerequisite to M1 functional reconnection through indirect pathways and to its efficacy in processing motor signals. The previous data suggest that different motor areas operate in parallel rather than in a hierarchical manner, and they are able to substitute for each other (Traversa et al., 1997; Loubinoux et al., 2003). Thus, remodeling of activation within a preexisting network may be an important process for recovery.

#### **4.3. Implication of somatosensory input as a rehabilitation strategy**

There is consensus on the efficacy of physiotherapy. Active training is more efficient than passive training, but active training cannot be applied to very impaired patients. We need to consider other approaches for patients who cannot move the paretic limbs at the early phase of recovery. Physiotherapists apply sensory stimulation and passive movement daily to

acute stroke patients and only these approaches are possible when the patients have complete paralysis. A few studies have validated the efficacy of sensory or proprioceptive stimulation on motor recovery.

Activation of Brain Sensorimotor Network by

Somatosensory Input in Patients with Hemiparetic Stroke: A Functional MRI Study 75

Another merit of massage or touch therapy may be the psychological effects produced by tactile stimulation, such as relaxation, alleviation of anxiety and depression. These effects may be evoked by stimulation of dopamine and serotonin secretion since increased levels of dopamine and serotonin have been shown in the urine following tactile skin stimulation (Field et al., 2005). Tactile stimulation in the rat evokes an increased dopamine release in the nucleus accumbens of the brain, which is thought to play a key role in motivational and reward processes (Maruyaka et al.; 2012). Relieving anxiety and depression seems important

The findings of this study demonstrate that the somatosensory inputs via the normal hand can activate brain sensorimotor network to a comparable extent with the areas that are activated during active hand movement, and that the somatosensory inputs via the paretic hand at the early stages of stroke before clinical motor recovery can also induce activities to some of the brain sensorimotor network. The result suggests that physiotherapy that employs somatosensory input via the paretic hand may be used as a first step to activate rehabilitation-dependent changes in the motor network in the brain toward restoration of motor function. The result may provide new insight into the establishment of rehabilitation

*Department of Neurology, International University of Health and Welfare Hospital, Nasushiobara,*

We thank the staff members of the MRI section of Sendai Nakae Hospital, Ms. Fumi Kozuka, Ms. Satsuki Ohi, Mr. Takeru Ohmukai, Ms. Yoko Sato, Ms. Aya Kanai, and Mr. Katsuhiro Aki, for their help to perform fMRI studies. We also thank Dr. Naohiro Saito, Department of Physiology, Tohoku University School of Medicine, Sendai, Japan, for his expert assistance on the fMRI-spm analysis. This study was supported by Grant-in-Aid for Scientific Research

Cao, Y.; D'Olhaberriague, L.; Vikingstad, E.M.; Levine, S.R. & Welch, K.M.A. (1998). Pilot study of functional MRI to assess cerebral activation of motor function after poststroke

in the early steps of rehabilitation for patients with acute stroke.

*Department of Neurology, Sendai Nakae Hospital, Sendai, Japan*

(22500473), Japan Society for the Promotion of Science.

hemiparesis. *Stroke*, 29, 112-122.

**5. Conclusion**

strategies after stroke.

**Author details**

Masahiro Izumiyama

**Acknowledgement**

**6. References**

Hiroyuki Kato

*Japan*

Carel et al. (2000) have shown that proprioceptive training induces a reorganization of sensorimotor representation in healthy subjects, and that the anatomical substrates are SMA and S1M1 contralateral to the stimulation. Subsequently, Dechaumont-Palacin et al. (2008) showed that paretic wrist proprioceptive training produced change in SMA, premotor cortex, and a contralesional network including inferior parietal cortex (lower part of BA 40), secondary sensory cortex, and PMv. Thus, increased contralateral activity in secondary sensorimotor areas may facilitate control of recovered motor function by simple proprioceptive integration in severely impaired patients. Brain activation during passive movement increase with time after stroke (Nelles et al., 1999; Loubinoux et al., 2003; Tombari et al., 2004). Nelles et al. (2001) tested a mixed, task-oriented rehabilitative program that is at first passive, then active as recovery permits, and observed hyperactivation of the bilateral low parietal cortex and premotor cortex and a smaller hyperactivation of the ipsilateral M1. Thus, the changes might represent increased processing of sensory information relevant to motor output.

Somatosensory input to the motor cortex, via corticocortical connections with the somatosensory cortex, is important for learning new motor skills (Sakamoto et al., 1989; Pavlides et al., 1993; Vidoni et al., 2010). Somatosensory input may also play a critical role in motor relearning after hemiparetic stroke (Dechaumont-Palacin et al., 2008; Conforto at al. 2007; Vidoni et al., 2009). Schaechter et al. (2012) showed that increased responsiveness of the ipsilesional S1M1 to tactile stimulation over the subacute posrstroke period correlated with concurrent motor recovery and predicted motor recovery experienced over the year. This finding suggests that a strong link between change in processing of somatosensory information in the S1M1 during the early poststroke period and motor recovery in hemiparetic patients.

Muscular and peripheral nerve electrical stimulation increases motor output after stroke (Conforto et al., 2002; Kimberley et al., 2004; Wu et al., 2006; Conforto et al., 2010). Peripheral nerve stimulation increases corticomotoneuronal excitability (Kaelin-Lang et al., 2002; Ridding et al., 2000), and activation of S1M1 and PMd in healthy subjects (Wu et al., 2005). If applied to paretic hand of stroke patients paired with motor training, electrical nerve stimulation may enhance training effects on corticomotoneuronal plasticity in stroke patients (Sawaki et al, 2006; Yozbatiran et al., 2006; Celnik et al., 2007).

Thus, increased activity in brain sensorimotor network by somatosensory input may facilitate control of recovered motor function by operating not only at a high-order processing level but also at a low level of simple sensory integration. Therefore, early poststroke fMRI studies using sensory stimulation as a task may be of great clinically importance and somatosensory stimulation over the poststroke recovery period may form a basis for improving motor recovery in stroke patients.

Another merit of massage or touch therapy may be the psychological effects produced by tactile stimulation, such as relaxation, alleviation of anxiety and depression. These effects may be evoked by stimulation of dopamine and serotonin secretion since increased levels of dopamine and serotonin have been shown in the urine following tactile skin stimulation (Field et al., 2005). Tactile stimulation in the rat evokes an increased dopamine release in the nucleus accumbens of the brain, which is thought to play a key role in motivational and reward processes (Maruyaka et al.; 2012). Relieving anxiety and depression seems important in the early steps of rehabilitation for patients with acute stroke.

### **5. Conclusion**

74 Novel Frontiers of Advanced Neuroimaging

stimulation on motor recovery.

information relevant to motor output.

hemiparetic patients.

acute stroke patients and only these approaches are possible when the patients have complete paralysis. A few studies have validated the efficacy of sensory or proprioceptive

Carel et al. (2000) have shown that proprioceptive training induces a reorganization of sensorimotor representation in healthy subjects, and that the anatomical substrates are SMA and S1M1 contralateral to the stimulation. Subsequently, Dechaumont-Palacin et al. (2008) showed that paretic wrist proprioceptive training produced change in SMA, premotor cortex, and a contralesional network including inferior parietal cortex (lower part of BA 40), secondary sensory cortex, and PMv. Thus, increased contralateral activity in secondary sensorimotor areas may facilitate control of recovered motor function by simple proprioceptive integration in severely impaired patients. Brain activation during passive movement increase with time after stroke (Nelles et al., 1999; Loubinoux et al., 2003; Tombari et al., 2004). Nelles et al. (2001) tested a mixed, task-oriented rehabilitative program that is at first passive, then active as recovery permits, and observed hyperactivation of the bilateral low parietal cortex and premotor cortex and a smaller hyperactivation of the ipsilateral M1. Thus, the changes might represent increased processing of sensory

Somatosensory input to the motor cortex, via corticocortical connections with the somatosensory cortex, is important for learning new motor skills (Sakamoto et al., 1989; Pavlides et al., 1993; Vidoni et al., 2010). Somatosensory input may also play a critical role in motor relearning after hemiparetic stroke (Dechaumont-Palacin et al., 2008; Conforto at al. 2007; Vidoni et al., 2009). Schaechter et al. (2012) showed that increased responsiveness of the ipsilesional S1M1 to tactile stimulation over the subacute posrstroke period correlated with concurrent motor recovery and predicted motor recovery experienced over the year. This finding suggests that a strong link between change in processing of somatosensory information in the S1M1 during the early poststroke period and motor recovery in

Muscular and peripheral nerve electrical stimulation increases motor output after stroke (Conforto et al., 2002; Kimberley et al., 2004; Wu et al., 2006; Conforto et al., 2010). Peripheral nerve stimulation increases corticomotoneuronal excitability (Kaelin-Lang et al., 2002; Ridding et al., 2000), and activation of S1M1 and PMd in healthy subjects (Wu et al., 2005). If applied to paretic hand of stroke patients paired with motor training, electrical nerve stimulation may enhance training effects on corticomotoneuronal plasticity in stroke

Thus, increased activity in brain sensorimotor network by somatosensory input may facilitate control of recovered motor function by operating not only at a high-order processing level but also at a low level of simple sensory integration. Therefore, early poststroke fMRI studies using sensory stimulation as a task may be of great clinically importance and somatosensory stimulation over the poststroke recovery period may form a

patients (Sawaki et al, 2006; Yozbatiran et al., 2006; Celnik et al., 2007).

basis for improving motor recovery in stroke patients.

The findings of this study demonstrate that the somatosensory inputs via the normal hand can activate brain sensorimotor network to a comparable extent with the areas that are activated during active hand movement, and that the somatosensory inputs via the paretic hand at the early stages of stroke before clinical motor recovery can also induce activities to some of the brain sensorimotor network. The result suggests that physiotherapy that employs somatosensory input via the paretic hand may be used as a first step to activate rehabilitation-dependent changes in the motor network in the brain toward restoration of motor function. The result may provide new insight into the establishment of rehabilitation strategies after stroke.

### **Author details**

Hiroyuki Kato *Department of Neurology, International University of Health and Welfare Hospital, Nasushiobara, Japan*

Masahiro Izumiyama *Department of Neurology, Sendai Nakae Hospital, Sendai, Japan*

### **Acknowledgement**

We thank the staff members of the MRI section of Sendai Nakae Hospital, Ms. Fumi Kozuka, Ms. Satsuki Ohi, Mr. Takeru Ohmukai, Ms. Yoko Sato, Ms. Aya Kanai, and Mr. Katsuhiro Aki, for their help to perform fMRI studies. We also thank Dr. Naohiro Saito, Department of Physiology, Tohoku University School of Medicine, Sendai, Japan, for his expert assistance on the fMRI-spm analysis. This study was supported by Grant-in-Aid for Scientific Research (22500473), Japan Society for the Promotion of Science.

#### **6. References**

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Calautti, C. & Baron, J.-C. (2003). Functional neuroimaging studies of motor recovery after stroke in adults. A review. *Stroke,* 34, 1553-1566

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**Section 2**

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**Chapter 5**

© 2013 Tran and Yonas, licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2013 Tran and Yonas, licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**Intraoperative Computed**

Additional information is available at the end of the chapter

Hoan Tran and Howard Yonas

http://dx.doi.org/10.5772/50895

**1. Introduction**

**Tomography/Angiography Guided**

It can be said that the history of neurosurgery has been interdependent with the history of concurrent technology, making it one of the more avante garde fields of medicine. Since the advent of monopolar cautery more than one hundred years ago, neurosurgeons have been rapid to apply the most recent technological advances to intraoperative uses to make surgery safer and less invasive. Since the first brain computed tomography was done in October 1971, neurosurgeons have quickly started adapting commercially available computed tomography machines for uses in the operating room to access some of the most formidable problems in neurosurgery: skull base lesions. In fact it was only 7 years later that computed tomography was adapted for intraoperative use with neuronavigation of intracranial lesions. Early publications of CT guided surgery already demonstrated benefits of real time data integration: navigation of eloquent areas of the brain, lesion localization, maximal lesion resection, immediate recognition of intraoperative hemorrhage, improved confidence for post operative care. Much of the work done was with cortical and subcortical lesions. Early CT scanners were very large, and competed with precious limited operating space; often scanners were placed near and operating suit and not necessarily within the operating suite. This necessitated transferring patients from the actually operating suite to the CT scanner prior to the completion of the case, in essence moving patients with open surgical wounds, on full mechanical ventilation, and other calculated risks. With improved computing, CT scanners increased in resolution and decreased in size, making it possible to obtain true intraoperative computed tomography, without exposing patients to all the risks of transferring a patient.

Neuronavigation is an invaluable tool to the modern skull base surgeon. Its ubiquity allows for safer and more effective treatment of lesions in difficult regions of the skull and brain. However because of brain shift once the calvarium is open, its ubiquity has its limitations.

**Resection of Skull Base Lesions**

**Chapter 5**

## **Intraoperative Computed Tomography/Angiography Guided Resection of Skull Base Lesions**

Hoan Tran and Howard Yonas

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/50895

### **1. Introduction**

It can be said that the history of neurosurgery has been interdependent with the history of concurrent technology, making it one of the more avante garde fields of medicine. Since the advent of monopolar cautery more than one hundred years ago, neurosurgeons have been rapid to apply the most recent technological advances to intraoperative uses to make surgery safer and less invasive. Since the first brain computed tomography was done in October 1971, neurosurgeons have quickly started adapting commercially available computed tomography machines for uses in the operating room to access some of the most formidable problems in neurosurgery: skull base lesions. In fact it was only 7 years later that computed tomography was adapted for intraoperative use with neuronavigation of intracranial lesions. Early publications of CT guided surgery already demonstrated benefits of real time data integration: navigation of eloquent areas of the brain, lesion localization, maximal lesion resection, immediate recognition of intraoperative hemorrhage, improved confidence for post operative care. Much of the work done was with cortical and subcortical lesions. Early CT scanners were very large, and competed with precious limited operating space; often scanners were placed near and operating suit and not necessarily within the operating suite. This necessitated transferring patients from the actually operating suite to the CT scanner prior to the completion of the case, in essence moving patients with open surgical wounds, on full mechanical ventilation, and other calculated risks. With improved computing, CT scanners increased in resolution and decreased in size, making it possible to obtain true intraoperative computed tomography, without exposing patients to all the risks of transferring a patient.

Neuronavigation is an invaluable tool to the modern skull base surgeon. Its ubiquity allows for safer and more effective treatment of lesions in difficult regions of the skull and brain. However because of brain shift once the calvarium is open, its ubiquity has its limitations.

© 2013 Tran and Yonas, licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2013 Tran and Yonas, licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Several modalities of imaging have developed into intraoperative uses, each with their own advantages and disadvantages. When used correctly, these tools provide powerful real time data needed to guide decision making, direct surgical approaches, and reveal lesional remnants. For skull base lesions, soft tissue discrimination and boney anatomy are both integral and necessary to execute resection of the most formidable lesions, while preserving the brain and its cranial nerves. We illustrate the feasibility of intraoperative computed tomography (CT) and CT angiography (CTA) in the resection of different skull base lesions with the following examples.

Intraoperative Computed Tomography/Angiography Guided Resection of Skull Base Lesions 85

**Figure 2. A**. Interosseus meningioma invading the petrous temporal bone, squamous temporal bone, and lateral orbital wall. **B**. Intraoperative CT showing residual tumor along the lateral orbital wall and petrous apex (arrows). **C**. Repeat CT shows complete removal and reconstruction of lateral orbital wall

and calvarium (arrows).

### **2. Methods/Results**

After the patient have undergone general anesthesia, a DORO head holder (ProMed Instruments, Freiburg, Germany) is used for head fixation, which is like the Mayfield except for radiolucent arms. The radiolucent arms are variable to fit the length of an individual's neck and are designed to fix the cranium but extend the fixation capability towards the shoulders. (Figure 1a). The shoulders are supported by the operating table, but the head and neck are suspended using the DORO head holding system to allow entrance into the CT gantry. Once a CT is warranted, the surgical field is draped with a large C-arm cover and the CT gantry is translated around the head. CT images are obtained, and reviewed within a few minutes as needed for integration into clinical management (Figure 1b).

**Figure 1. A.** The DORO head holder is a Mayfield-type holder but with extensions that are radiolucent. **B.** Ceretom is translated over the patients head during surgery without moving patients.

*Case 1*

This patient is a 39 year old female who had noticed for at least eight years a growing hard prominence on the left side of her face. She also noted proptosis that had been getting worse for the last year accompanied by headaches that were increasing in frequency and intensity. Routine CT brain and MRI brain was consistent with an interosseous meningioma and a superficial biopsy confirmed the diagnosis. The tumor involved the left middle fossa and

Intraoperative Computed Tomography/Angiography Guided Resection of Skull Base Lesions 85

84 Novel Frontiers of Advanced Neuroimaging

with the following examples.

**2. Methods/Results**

*Case 1*

Several modalities of imaging have developed into intraoperative uses, each with their own advantages and disadvantages. When used correctly, these tools provide powerful real time data needed to guide decision making, direct surgical approaches, and reveal lesional remnants. For skull base lesions, soft tissue discrimination and boney anatomy are both integral and necessary to execute resection of the most formidable lesions, while preserving the brain and its cranial nerves. We illustrate the feasibility of intraoperative computed tomography (CT) and CT angiography (CTA) in the resection of different skull base lesions

After the patient have undergone general anesthesia, a DORO head holder (ProMed Instruments, Freiburg, Germany) is used for head fixation, which is like the Mayfield except for radiolucent arms. The radiolucent arms are variable to fit the length of an individual's neck and are designed to fix the cranium but extend the fixation capability towards the shoulders. (Figure 1a). The shoulders are supported by the operating table, but the head and neck are suspended using the DORO head holding system to allow entrance into the CT gantry. Once a CT is warranted, the surgical field is draped with a large C-arm cover and the CT gantry is translated around the head. CT images are obtained, and reviewed within a

**Figure 1. A.** The DORO head holder is a Mayfield-type holder but with extensions that are radiolucent.

This patient is a 39 year old female who had noticed for at least eight years a growing hard prominence on the left side of her face. She also noted proptosis that had been getting worse for the last year accompanied by headaches that were increasing in frequency and intensity. Routine CT brain and MRI brain was consistent with an interosseous meningioma and a superficial biopsy confirmed the diagnosis. The tumor involved the left middle fossa and

**B.** Ceretom is translated over the patients head during surgery without moving patients.

few minutes as needed for integration into clinical management (Figure 1b).

**Figure 2. A**. Interosseus meningioma invading the petrous temporal bone, squamous temporal bone, and lateral orbital wall. **B**. Intraoperative CT showing residual tumor along the lateral orbital wall and petrous apex (arrows). **C**. Repeat CT shows complete removal and reconstruction of lateral orbital wall and calvarium (arrows).

hyperostosis of the squamous temporal bone, the petrous temporal bone, sphenoid wing, the zygomatic process, and the lateral orbital wall.

Intraoperative Computed Tomography/Angiography Guided Resection of Skull Base Lesions 87

**Figure 3. A**. Left superior cerebellar AVM seen on T1 weighted MRI axial (left), sagittal (upper insert),

and coronal view (lower insert). **B**. Intraoperative CT of partially resected AVM juxtaposed to preoperative embolization not visualized under microscope. **C.**