Several decades of research with electromagnetic brain signals, but also with fMRI and PET, have consistently demonstrated that specialized brain regions respond to external stimulation and tasks.
These so-called activations can consist of potential changes, or local increases in oscillatory power, metabolism, and oxygen availability. They are obtained by subtracting control or baseline conditions from an active task condition of interest. The success of this approach has led to a view of the brain as a collection of specialized areas which are specifically recruited during, and responsible for, certain specific tasks. Systematic studies of the consequences of acquired brain lesions in patients have further corroborated this concept by showing that circumscribed brain damage can lead to relatively specific and reproducible behavioral deficits. For instance, speech production leads to an activation
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of the left posterior inferior frontal gyrus and damage to this area leads to a so-called Broca aphasia consisting of difficulties with speech production.
However, this view emphasizing the functional segregation of brain areas neglects the fact that these areas are massively and reciprocally interconnected (Sporns et al. 2004). Brain lesions therefore not only induce loss of local neural function, but also distant changes in a larger network of brain interactions (Honey and Sporns 2008; Alstott et al. 2009). Similarly, task-induced brain activations are rarely limited to a single brain region but typically comprise an interacting network. It has long remained unknown how the massive structural connectivity of the brain influences brain function, but advances in signal analysis in the last 10 years have enabled new technical tools and led to a paradigm shift in our understanding of brain function and disease.
6.1 FUNCTIONAL CONNECTIVITY
While the term structural connectivity refers to neural fiber tracts connecting brain regions, the term functional connectivity (FC) describes interregional neural communication. Interregional neural communication is thought to be accompanied by a synchronization of oscillations between different brain regions (Aertsen et al. 1989; Gray et al. 1989; Gray and Singer 1989; Engel et al. 1992). FC can therefore be quantified with measures of similarity and synchronization between activities in different brain regions. If two or more regions show similar and synchronous activity they are considered to be interacting and communicating, i.e., to be functionally connected. Numerous algorithms for calculating FC have been introduced (e.g. Nunez et al. 1997; Lachaux et al. 1999; Gross et al. 2001; Stam et al. 2003; Nolte et al. 2004; Pascual-Marqui et al. 2011) with each having its advantages and disadvantages depending on the context and the question asked.
EEG researchers have been the first to try to characterize functional network interactions by measuring coherence between EEG electrodes (Lopes da Silva et al. 1973; Thatcher et al. 1986).
However, indices of functional interaction calculated between surface electrodes cannot be attributed to brain regions of interest or to structural lesions. Field spread leads to a wide representation of sources in many sensors, which makes the interpretation of functional connectivity measures between sensor pairs difficult (Srinivasan et al. 2007; Schoffelen and Gross 2009). Only when fMRI studies started to look at functional network interactions, it became clear that they have a distinct and consistent spatial configuration.
6.2 RESTING-STATE NETWORKS IN FMRI
When fMRI is recorded at rest, it contains spontaneous slow signal fluctuations. During many years, these resting-state fluctuations were treated as noise and subtracted out in the analyses. However, Biswal et al. (1995) observed that they were in fact highly correlated among regions belonging to the motor network, but not between motor and non-motor areas. Subsequent studies confirmed that spontaneous fluctuations of activity in the resting brain are highly organized and coherent within specific neuro-anatomical systems (Greicius et al. 2003; Fox et al. 2005; Damoiseaux et al. 2006). For instance, the bilateral intraparietal sulcus and frontal eye fields, which are thought to mediate spatial attention, show highly correlated fluctuations among each other but not with other unrelated areas.
These patterns of coherence are task-independent and can also be studied at rest (Greicius et al.
2003) for which reason they are named resting-state networks. The topography of resting-state networks matches the topography of brain activations induced by corresponding tasks (Vincent et al.
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2007; Zhao et al. 2011) and the strength of coupling in a given network is linearly related to behavioral performance in tasks relying on this network (Wang et al. 2010; Koyama et al. 2011).
These findings demonstrate that resting-state fluctuations in fMRI are not noise but physiological brain activity carrying important information about brain organization. More importantly, they change our conception of the brain, which turns out to not simply activate in segregated areas during tasks but to have a high intrinsic organization into functional networks (Raichle 2011) which predetermines individual responses to external stimuli. Indeed, the intrinsic activity seems to consume the vast majority of the brain’s energy budget (Raichle and Mintun 2006).
6.3 FUNCTIONAL BRAIN NETWORKS IN MEG AND EEG
The success of network analyses in fMRI also led to renewed interest for these measures in the EEG and MEG community. By now, the availability of inverse solutions allowed to overcome the original problems arising with analyses of FC between EEG and MEG sensors. It became possible to estimate neural resting-state oscillations in the grey matter from surface recordings and to calculate FC between actual brain areas rather than between sensors (Gross et al. 2001; Schoffelen and Gross 2009).
Appendix VI reprints our paper in which we describe the detailed methods for EEG and demonstrate that coherent networks can be reconstructed with sufficient accuracy from EEG recordings (Guggisberg et al. 2011b). Other studies made similar demonstrations both for EEG (Astolfi et al.
2005; Pascual-Marqui et al. 2011) and MEG (Gross et al. 2001; Schoffelen et al. 2008). The software I wrote for assessing networks with EEG or MEG is freely available as part of our open-source toolbox for Matlab (NUTMEG: http://nutmeg.berkeley.edu/) (Dalal et al. 2011).
The reconstruction of cortico-cortical network interactions with EEG and MEG has a severe potential methodological confounder that needs to be controlled. The potential arising in a given grey matter source spreads with light speed throughout the brain and the scalp. This spread is called volume conduction and is in fact the reason we are able to record EEG and MEG. Inverse solutions try to invert the spreading effect of volume conduction and to reconstruct the location of the original generators of the measured electromagnetic field. However, even the best available inverse solutions for reconstructing distributed activity have limited spatial resolution. This means that the current reconstructed at a given brain location arises in fact not only from activity at this source but is additionally influenced by activity in adjacent locations. This effect is called spatial leakage and entails that neural activity reconstructed at a given location is a linear combination of activity from a region surrounding this location. The extent of this surrounding region is larger for inverse solutions with low spatial resolution such as Minimum Norm Estimates (Hamalainen and Ilmoniemi 1994) and its variants than for instance for beamformers (Van Veen et al. 1997; Sekihara et al. 2004), but spatial leakage is present even for beamformers. It introduces artificial similarity between adjacent brain regions and hence massively biases the estimates of FC. Moreover, the spatial resolution of inverse solutions and hence their spatial leakage is not homogeneous throughout the brain but worse for deep brain areas. Overall, this leads to a massive overestimation and distortion of the magnitude of network interactions. We resolved this problem by using the imaginary component of coherence (IC) (Nolte et al. 2004) as index of FC (Guggisberg et al. 2008b). IC exploits the fact that, since volume conduction travels with light speed, similarities among time series arising from spatial leakage occur with near zero time delay. We can therefore omit the real component of coherence which mostly contains similarities with zero time lag and use only interactions occurring with a certain time lag
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which are unaffected by spatial leakage and represented by IC (Nolte et al. 2004). This approach has recently been formally validated (Sekihara et al. 2011). Other corrections for spatial leakage are also available (Stam et al. 2007; Brookes et al. 2011a; Brookes et al. 2011b; Ghuman et al. 2011; Pascual-Marqui et al. 2011; Schoffelen and Gross 2011).
Simultaneous recordings of resting-state fMRI and EEG have shown that if the time course of spontaneous EEG oscillations is convolved with a hemodynamic response function and used as regressor for fMRI analysis, the resulting BOLD responses localize to similar distributed networks as found in fMRI network studies (Jann et al. 2009; Britz et al. 2010; Musso et al. 2010). Interestingly, the closest spatial match to fMRI networks was found with EEG/MEG networks reconstructed from slow cortical potentials and beta-/gamma power fluctuations (He et al. 2008; Brookes et al. 2011a;
Brookes et al. 2011b). Hence, the spatial configuration of fMRI resting-state networks can also be observed in direct recordings of neural oscillations.
6.4 CLINICAL APPLICATIONS
Network analyses allow investigating the functional organization of the entire brain while merely requiring a single resting-state recording. This resolves many of the problems and limitations that are inherent to traditional functional imaging studies, in which patients have to perform specific tasks which are designed to activate brain regions of interest. The ability of patients to correctly perform these tasks is often limited. This is particularly evident in studies that address questions related to neurological deficits and their recovery. E.g., studies assessing the reorganization of the motor cortex in patients with hemiplegia require the patients to perform repetitive movements which is precisely the task they cannot accomplish in a sufficiently controlled manner due to their deficit (Weiller et al.
1993). Even if patients who are able to perform the task are selected or very simple paradigms are designed, they can only activate a circumscribed set of brain areas, which does not adequately represent the intrinsic brain architecture. Resting-state analyses have the advantage of being able to assess several brain systems with one single recording requiring almost no patient collaboration.
My collaborators and I have had the opportunity to investigate networks in several patient groups with brain disease and healthy volunteers. The particular interest of this research was to look for and validate electromagnetic markers of clinically relevant network function. The ultimate goal is to obtain a new diagnostic tool which helps in diagnosis, monitoring, and treatment selection for brain disease. We have discovered resting-state oscillation coherence in the alpha frequency band (ABC) as a novel biomarker of clinically relevant brain network function.
6.5 ALPHA-BAND COHERENCE
Our first study investigating clinical network function was done with MEG in patients with brain tumors (Guggisberg et al. 2008b). It is reproduced in Appendix VII. As briefly described above, resting-state oscillations in the brain were reconstructed with an inverse solution from the recordings of MEG sensors. From this, we could calculate the FC between all possible combinations of voxel pairs at several oscillation frequencies. This setting confronted us with a highly multidimensional data array. Since it was the first clinical study performing network imaging from electrophysiological oscillations in the source space, we had no a priori knowledge about what aspects in network interactions might be affected by the tumors. We therefore had to use an exploratory approach, which we guided by the following reflection: tissue that is necrotic or structurally disconnected due to the tumor cannot participate in the dynamic interactions occurring
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between intact brain areas and would therefore be expected to show decreased FC with the rest of the brain. In order to detect such a decreased FC to the rest of the brain, we did not have to look at all combinations of connections between voxel pairs separately, but it was sufficient to estimate it with the average FC of each voxel to all other voxels. This enabled a first reduction of data dimension. The next data reduction step consisted in finding out in which oscillation frequencies the tumors affected network interactions. To this end, we calculated a FC spectrogram for the interactions between tissue around the tumor and the rest of the brain and compared it to spectrograms of age-matched healthy subjects and of the unaffected contralateral hemisphere. Only ABC (7-13 Hz), but not coherence in other frequency bands was significantly different between patients and controls. Hence, we focused our reconstructions of network interactions to the alpha frequency band. We calculated maps of the mean ABC between each voxel to the rest of the brain and observed that they were able to distinguish between tumor infiltrated brain regions that are still functionally intact from regions that are damaged. Dysfunctional tumor areas showed indeed the expected functional disconnection from the rest of the brain, which was not the case for areas with preserved function. Patients with lesion-induced neurological deficits displayed decreased connectivity estimates in the corresponding brain area compared with intact contralateral regions. In tumor patients without preoperative neurological deficits, brain areas showing decreased coherence could be surgically resected without the occurrence of postoperative deficits.
Appendix VIII reproduces a follow-up study (Martino et al. 2011) on 57 tumor patients, which compared our non-invasive maps of ABC to invasive, intraoperative electrical stimulation (IES) which is the gold standard for defining the borders of tumor resections while avoiding neurological deficits.
It confirms that ABC maps have an excellent negative predictive value of 100%, which means that in patients with decreased connectivity in the entire tumor area, the probability of a negative IES mapping was 100%. Hence, they may be able to replace invasive procedures in certain patients with brain tumors.
The findings of these initial studies strongly suggested that ABC is related to neurological function but the evidence was somewhat indirect and derived only from brain areas surrounding tumors.
Furthermore, the precise relationship between network coherence and behavior in different oscillation frequencies had remained unclear. We addressed these questions in a population of 20 stroke patients (Dubovik et al. 2012). We scanned the entire cortex for the magnitude of FC to the rest of the brain in all oscillation frequencies from 1 to 20 Hz. In addition, we quantified language, spatial attention, and motor deficits of the patients with a battery of standardized clinical tests. This enabled us to compare network function and clinical performance across several left and right hemispheric brain regions. The paper reporting these results is reproduced in Appendix IX. Similarly as in the tumor patients, we found that brain regions that are normally responsible for deficient functions were disconnected from the rest of the brain, in the alpha frequency band. For instance, a patient with Broca’s aphasia showed decreased alpha-band connectivity between Broca’s area and the rest of the brain. More importantly, the disruption of coherent electrical oscillations in the alpha band at rest was linearly correlated with the severity of neurological deficits. This demonstrates that behavior of patients after stroke is linearly associated with the magnitude of ABC between the main node responsible for a given function and the rest of the brain. Additional tests confirmed that this association was driven by the phase similarity between regions and independent of anatomical lesions or amplitude of neural activity. We have also observed a linear association between behavior and ABC in healthy subjects (Dubovik et al. in preparation; Dubovik et al. in press), thus confirming
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that the association is independent of the presence of brain lesions. Hence, a decrease in ABC does not merely reflect a loss of local neural tissue, but network dysfunction.
In sum these results consistently demonstrate a linear association between behavior and ABC across several different populations. In order to further validate ABC as biomarker of network function, we assessed how changes in ABC translate into behavioral changes and vice versa. In patients with stroke, improvement of cognitive and motor deficits during intensive rehabilitation programs were found to be linearly correlated with an increase in alpha-band coherence between brain regions mediating the trained function and the rest of the brain (Westlake et al. in press).
We can now start to use the new marker for investigating network plasticity. In patients with Alzheimer’s disease (AD), neuronal loss is found to be associated with a severe reduction of ABC in the same brain regions that are also most severely affected by atrophy and amyloidal plaques.
However, AD patients also show adaptive network reorganization allowing relatively preserved cognitive functioning (Dubovik et al. in press). In addition, ABC was able to reveal the network correlates of positive and negative symptoms in patients with schizophrenia (Hinkley et al. 2011).
Resting-state coherence in other oscillation frequencies was not linearly correlated with behavior, which does however not mean that they are not relevant. Indeed, other studies have observed relevant interactions in the beta band (13-30 Hz) during tasks (Gerloff et al. 2006) and when assessing FC within regions belonging to the same network (Brookes et al. 2011b) rather than between a target region and the rest of the brain. Network interactions in the theta frequency band may be important during development and for long-term memory (Dubovik et al. in press).
6.6 FUTURE DIRECTIONS
Our previous findings inspire in particular the following 4 lines of research.
1. ABC and possibly other measures of network function have excellent potential in being able to gain insights into individual neural states of patients with brain disease and to guide prognosis and treatment decisions in the future. In order to fully exploit this potential we aim to further develop the tools for assessing brain networks after brain damage. In particular, we aim to investigate how ABC is dependent on structural connections between brain area, and how it compares to fMRI network measures.
2. Network imaging provides new technology for elucidating brain mechanisms of recovery from neurological deficits. The question of how the brain can repair itself has traditionally been investigated with longitudinal studies of motor activation with rather conflicting results (reviewed in Cramer (2008)). Network imaging has excellent potential for providing crucial new insights into the plastic brain, in particular with regards to the role of local, perilesional reorganization vs.
distant, contralateral network effects for clinical recovery.
3. Based on the observation that clinical performance of patients with brain lesions is linearly correlated with the magnitude of coherence in their neural networks, I hypothesize that treatments which increase network coherence have beneficial effects in these patients. Ongoing projects therefore aim to manipulate network coherence with different treatment strategies and assess the resulting clinical response.
4. We will investigate whether network states before treatment allow predicting the clinical response of individual patients to standard and new treatments. Based on previous reports
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(Hampson and Hoffman 2010; Vanneste et al. 2011) and own pilot studies, I hypothesize that the magnitude of network FC before treatment predicts treatment-induced change in network function and hence the clinical effect.
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