Available online at www.sciencedirect.com
ScienceDirect Neural-Event-Triggered fMRI of large-scale neural networks Nikos K Logothetis Brains are dynamic systems, consisting of huge number of massively interconnected elementary components. The activity of these components results in an initial conditionsensitive evolution of network states through highly nonlinear, probabilistic interactions. The dynamics of such systems cannot be described merely by studying the behavior of their components; instead their study benefits from employing multimodal methods. Neural-EventTriggered (NET) fMRI is a novel method allowing identification of events that can be used to examine multistructure activity in the brain. First results offered insights into the networks that might be involved in memory consolidation. On-going work examines the physiological underpinnings of the up and down modulation of metabolic activity, mapped with this methodology. Addresses Max Planck Institute for Biological Cybernetics, Imaging Science and Biomedical Engineering, University of Manchester, Manchester, United Kingdom Corresponding author: Logothetis, Nikos K ([email protected])
Current Opinion in Neurobiology 2015, 31:214–222 This review comes from a themed issue on Brain rhythms and dynamic coordination Edited by Gyo¨rgy Buzsa´ki and Walter Freeman
http://dx.doi.org/10.1016/j.conb.2014.11.009 0959-4388/# 2014 Published by Elsevier Ltd.
Introduction Neural activity patterns related to behavior occur at many different spatiotemporal scales and on different levels, ranging from molecules, genes and cells to neuronal microcircuits and large-scale neuronal networks that often involve distinct brain nuclei and regions. A computationally useful description of the hierarchical levels of organization considers the degree of covariance between neurons and the population size and extent of local neighborhoods [1,2]. The microscopic and mesoscopic levels predominantly refer to spiking and integrative dendritic activity, respectively. Macroscopic activity informs us about whole brain dynamics and interactions Current Opinion in Neurobiology 2015, 31:214–222
between large-scale neural systems, such as structures, nuclei and cortical regions. A predominant methodology for studies at the microscopic and mesoscopic-levels involves the use of single or multiple unit recordings. In these recordings, neuronal activity is often compared with sensory processing or behavior [3]. The development and optimization of electrode arrays and various intracranial imaging methods now permits recordings from an increasingly greater number of neurons, which provides information on larger neuronal ensembles [4]. In addition to microscopic and mesoscopic measurements, non-invasive brain imaging methods, such as electroencephalography (EEG), magnetoencephalography (MEG), positron emission tomography (PET) and functional magnetic resonance imaging (fMRI), are also used. These methods can be used in combination with biophysical modeling and multivariate pattern classification and decoding techniques to understand large-scale connectivity and the interactions of various neural structures and areas constituting the brain macroscopic system [5]. Improving our understanding of the function of neurons and microcircuits is important. Furthermore, understanding the anatomical and functional connectivity of large-scale networks is critical. The study of the brain’s complexity would also benefit from developing and employing methods combining simultaneous small and large-scale recordings of neural activity. Brains are complex dynamic systems par excellence. The brain system consists of many massively interconnected elementary components. The activity of these components results in an initial condition-sensitive evolution of network states through highly non-linear, probabilistic interactions. The dynamics of such systems cannot be described merely by studying the behavior of their elementary components. The system reflects a synergy-induced emergence of global patterns of neural activity. The study of these patterns requires the simultaneous acquisition of microscopic information at many different sites of brain networks. A spatial high resolution functional MRI scan with parallel imaging based on phased arrays of radiofrequency coils and reconstruction algorithms for acceleration volumes of 22 slices with 128 128 pixel in-plane resolution can be acquired with a temporal resolution of 0.5–1 s. It would undoubtedly be better to have 360 448 microelectrodes in a virtual three-dimensional grid within the brain rather www.sciencedirect.com
Neural-Event-Triggered fMRI of large-scale neural networks Logothetis 215
than tracking the metabolic changes in each voxel. However, this advance is unlikely to be achieved in the near future. The relationship between voxel-intensities and local field potentials is complicated and far from proportionate. Thus, the images require very careful interpretation [3]. The highly multidimensional information provided by fMRI may eventually provide useful constraints in the interpretation of brain states related to simultaneously acquired physiological data. These constraints can also be used in subsequent electrophysiological studies for an informed selection of brain regions in multisite recordings. This review briefly summarizes several characteristic indicators of brain-network selforganization and subsequently describes a first attempt to use so-called Neural-Event-Triggered fMRI (NETfMRI) for gaining insights into the brain states associated with various spontaneous neural events in different brain structures. Lastly, I briefly discuss problems with the interpretation of MR imaging data and the future perspectives of the NET-fMRI methodology.
Self-organized brain activity Over the last decade, the focus of neuroimaging studies has shifted from functional localization to functional integration and brain connectivity [5]. The seminal studies of Raichle and colleagues on the default mode network [6] provided analyses of data from resting-state fMRI. These studies demonstrated the existence of various types of statistical dependencies between separated brain regions that reflect intrinsic functional connectivity. Structural and functional MRI data have been analyzed with increasingly rigorous and refined approaches of statistical mathematics and theoretical physics, including graph-theoretical methods and modeling in the framework of complex dynamic systems [2,7]. Systems neuroscience has a long history of electrophysiological investigations of spontaneous microscopic and mesoscopic neural activity in humans and animals. A characteristic emerging property in many brain networks is oscillatory activity [1,8]. This activity has been studied in great detail in various systems, including the thalamocortical system, basal ganglia, hippocampal formation and the brainstem. Rhythmic activity often reflects the interactions of populations of neurons [9,10]. However, in the case of the thalamocortical networks, it may also be generated by single neurons as a result of interplay between specific intrinsic currents [11]. The oscillations may occur in the same brain state and interact with each other either within the same or across different brain regions. These interactions are commonly hierarchical, and the phase of slower oscillations often modulates the power of the faster oscillations [12,13]. The spontaneous activity patterns largely emerge from the very same networks underlying various behaviors. Therefore, it is not surprising that on-going activity often constrains or determines responses to sensory stimulation [14]. Moreover, www.sciencedirect.com
frequency-band selective modulation [15] and large-scale synchronization [16] of rhythmic activity are believed to play a crucial role in cognitive processing. Brain oscillations are similar to most biological rhythms and are weakly chaotic. Their frequency range extends from infraslow (300 mV) LFP deflections. The first detailed description PGO came from electrophysiological recordings in pons, LGN and the cortex of cats [35,36]. PGO waves are related to several important brain functions including sensorimotor integration, dreaming, development and learning [37]. Their relation to large-scale networks has been recently demonstrated in human fMRI studies reporting REM-related activations in the pontine tegmentum, ventro-posterior thalamus and the primary visual cortex in the absence of any visual input [38].
Spontaneous events and multi-structureactivity (MSA) profiles Decades of experimental work in animals and humans suggest that the aforementioned oscillatory patterns, inCurrent Opinion in Neurobiology 2015, 31:214–222
cluding single or multiple cycle short-lasting episodes, reflect state changes of self-organizing large-scale networks. These patterns regulate cognitive capacities, such as learning, memory encoding and consolidation and memory-guided decision making [39,40,41,42]. It is known that reactivation of neuronal ensembles that were active during awake experiences occur primarily during ripples [43–46]. Thus, the number of ripples increases after learning and the increase appears to predict memory recall both in rats [47,48] and in humans [49]. Conversely, the elimination of ripples by the electrical stimulation of HP during the post-learning SWS interferes with memory consolidation [50,51]. Similarly, disruption of PGO and REM-sleep appears to selectively interfere with the retention of procedural knowledge [52]. Evidently, the exact topologies of such networks and the emerging dynamic activity patterns resulting from their evolution over time are likely to be excellent indicators of specific brain functions and dysfunctions. However, electrophysiological recordings of neural events commonly involve one or more brain regions chosen by means of their anatomical connectivity patterns or by formerly established cooperative interactions of structures in the context of behavior. The global states associated with events remain elusive because of the dearth of methodologies permitting concurrent local recordings and wholebrain activity mapping. In an attempt to study the topology and dynamics of networks associated with events such as ripples, spindles or PGO waves, we have recently developed and applied so-called neural event triggered functional magnetic resonance imaging (NET-fMRI). This method permits concurrent multi-structure and multi-site intracranial recordings and the fMRI of whole-brain activity in 60– 70 regions of interest [ROI] corresponding to various cortical areas and subcortical structures or nuclei. These recordings can be performed in anesthetized and awake monkeys [53]. Figure 1 displays the ripple-triggered whole-brain activity map, the average time course of each ROI and the fraction of its voxels that were significantly up or down modulated within a peri-event time window. The fractional modulated volume and response-dynamics of each ROI, here termed as multi-structure-activity (MSA), offers some insights into the state of the brain networks associated with an event such as SPW-R. Sorting ROIs according to the developmental categorization of brain structures (e.g., metencephalon, mesencephalon, diencephalon, limbic and neo-cortex) revealed a modulation dichotomy between cortical and subcortical areas. SPW-Rs were consistently associated with robust activations of the neocortex and limbic cortex that took place concurrently with robust negative BOLD responses (NBR) in subcortical thalamic, associational (e.g., basal ganglia, cerebellum) and midbrain-brainstem neuromodulatory structures. Such downregulation of activity www.sciencedirect.com
Neural-Event-Triggered fMRI of large-scale neural networks Logothetis 217
Figure 1
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Ripple-Triggered MSA. (a) Time–frequency representation of the SPW-R events. (b) Average activation maps from all of the anesthetized-monkey sessions. PBR is observed in the neocortex and limbic cortex, while activity suppression is seen in the diencephalon, mesencephalon and metencephalon. (c) Time courses of each group ROI. Note the sign change in the transition from cortical to subcortical areas and the differences in response onset. (d) Fractions of activated voxels for each group ROI.
in many different structures at the time of SPW-R occurrence may be due to a state-transition favoring optimal hippocampal–cortical communication. The thalamic NBR might indicate an optimal brain-state during which the signal-to-noise ratio of hippocampal– cortical communication is maximized by minimizing neural activity related to sensory processing. The strong downregulation of metabolic activity was also observed in large portions of associational subcortical brain www.sciencedirect.com
structures, such as the basal ganglia (BG), which are related to the acquisition of skills and habits. BGsuppression likely involves a complex interplay of excitatory and inhibitory afferents, such as the medial temporal and pallido-thalamocortical projections to the prefrontal cortex or the subicular projections to the striatum [54]. In addition, activity suppression in BG may be due to diverse neuromodulatory regulation mechanisms, such as the differential dopaminergic (SN/VTA) or amygdala regulation of hippocampal and Current Opinion in Neurobiology 2015, 31:214–222
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striatal activities [55,56]. Similarly, the deactivation of the pontine nuclei and cerebellar cortex could result from excitatory cortico-pontine or inhibitory subcortical projections onto specific subsets of GABAergic or glutamatergic pontine neurons, respectively [57]. An occasional concomitant activation (rather than deactivation) of the deep cerebellar nuclei could result from the deactivation of the inhibitory cortico-nuclear projections of the Purkinje neurons [57] and excitatory projections from various subcortical nuclei linked to the declarative memory system. All of these processes could optimize declarative memory consolidation by potentially reducing interfering activity related to other types of memory. Several studies have reported competition between learning and memory systems during the execution of various learning tasks [58,59]. There is evidence of post-learning antagonistic
interactions between memory systems in animal lesion and hippocampal inactivation studies [60]. The NETfMRI findings indicate that competition between memory systems may take place at the time of SPW-R occurrence during off-line states. The strong NBR of pons, LGN and foveal V1 activity during SPW-Rs occur despite the overall positive BOLD responses in the other primary sensory and associational cortices. These responses may be related to suppression of cholinergic neurons in the brainstem, including those inducing the PGO waves during the hippocampal–cortical dialog. REM and PGO are both related to procedural learning and synaptic consolidation. The network states related to synaptic and system consolidation may have synergistic or antagonistic interactions depending on learning/encoding phase, short-term retention of information, or long-term consolidation. The NET-fMRI
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MUA and BOLD Responses in Hippocampus (left panels) and the dorsal Lateral Geniculate Nucleus (right panels). Blue (62%) and dark-red (38%) traces are the means of two subsets of ripple-triggered neural or fMRI activity obtained by clustering the dLGN responses. (a) Ripple-triggered MUA responses in the pyramidal cell layer of the hippocampus. (b) MUA in dLGN. (c) BOLD average BOLD response of the HP-ROI, and (d) BOLD response of the dLGN-ROI. Insets show the BOLD modulation and electrode position for each structure.
Current Opinion in Neurobiology 2015, 31:214–222
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results offer some first global-hints and any serious explanation of the underlying physiological process will require further research.
Interpretation of the up- and down-regulation of metabolic activity The NET-fMRI results raise the question of correct interpretation of fMRI signals, which correlate with changes in metabolism rather than neural activity. MR signal interpretation is more complicated during spontaneous activity. Oscillations and synchrony could, in principle, reduce the metabolic requirements by increasing the synaptic efficiency. However, the oscillations and the gamma rhythm are associated with high-energy demands that require high oxidative energy metabolism, strong mitochondrial performance, and sufficient supply with oxygen and nutrients [61,62]. In general, the variance of positive BOLD responses (PBR) in the cortex is best explained by the dynamics of different band-limited-power signals derived from the LFP in anesthetized or drug-free animals. The interpretation of the LFP itself may also require caution because it reflects both sensory input and neuromodulation-induced changes in the local excitation-inhibition balance [63–65]. However, the relationship of neuronal inhibition and BOLD responses is not straightforward. The inhibition may increase or decrease energy consumption depending on the extent of local interneuron involvement [3]. Certain interneuronal classes directly control blood flow regulation [66], and the hemodynamic mechanisms of NBR are different from those of PBR [67]. Nonetheless, NBR itself may be seen as a reasonable marker of reduction of population-activity because it is often correlated with decreases in multiunit activity (MUA) [3,68]. On-going experiments in our laboratory with combined hippocampal, thalamic, cortical recordings and MR imaging show a robust correlation between up/down modulations and changes in MUA activity (Figure 2). Figure 2a,b shows ripple-triggered multiunit activity in the hippocampus and lateral geniculate nucleus, respectively. The responses in both structures were subdivided into two groups by clustering. Blue and dark-red traces show responses occurring in 62% and 38% of ripple-episodes, respectively. Figure 2c,d shows the corresponding BOLD responses. In both subtypes, there is a robust reduction in thalamic MUA, which fully supports the structure’s NBR. At first glance, this finding appears to be in disagreement with a large number of observations, suggesting certain degrees of synchrony between hippocampal ripples and thalamocortical spindles [10]. However, the slow MUA reduction often occurs with a brief, ripple-synchronized increase in MUA (see dark-red trace in Figure 1b). Therefore, we hypothesize that a plasticity-related activity enhancement of case selective neuronal subwww.sciencedirect.com
populations may occur concurrently with an overall decrease in population spiking. This strategy would increase the SNR of targeted processes. The decreases in MUA were also observed in some other brain regions with characteristic NBR, including the primary visual cortex, associational thalamus and pontine region. Cumulatively, these observations suggest that in most NBR areas revealed with NET-fMRI a reduction of MUA is highly probable and is potentially associated with diverse short-lasting activity changes at the time of SPW-R.
Conclusions and perspectives NET-fMRI is a new method that allows events to be detected, identified and used to examine MSA in the entire brain. Its application may prove highly informative even if the initial results are suggestive rather than decisive regarding the comprehension of network topology and dynamics. It should be noted that neither the activity maps nor the sequences of up and down-regulation indicate a causal relationship between the trigger event and the network activity changes. The state of widespread networks likely depends on a large number of variables. A subset of these variables may be gradually characterized following intensive future experimentation. Interestingly, large-scale observations may improve our understanding of the detailed nature of single neural episodes. SPW-R events are associated with the strong activation of the entire cortex. Is each event correlated with such extensive upregulation? In principle, global activation may simply be the result of averaging hundreds of events that individually have specific cortical targets. Alternatively, events may have different subtypes that each reflect different hippocampal networks and are associated with different MSA and different cognitive functions. SPW-R events have mainly been associated with memory consolidation. However, recent experimental evidence suggests that they are involved in other cognitive capacities, such as memory-driven decision making and working memory [41,69,70]. Such functions may be mediated by different networks and neuronal types. The connectivity patterns and dynamics of these networks could potentially influence the time-frequency characteristics of SPW-R. As mentioned above, SPWRs emerge from the interactions of CA3 and CA1 neurons. The synchronized bursting of CA3 neurons (inducers) yields a large LFP depolarization in the CA1 dendritic fields. The resulting interaction of excitatory–inhibitory cells in CA1 (generators) produces fast oscillation. The effects of inducers and generators are evident in the time– amplitude–frequency characteristics of sharp waves and ripples, respectively. Quantitative analysis of the NET-fMRI data revealed four subtypes of SPW-Rs differing in the relationship of Current Opinion in Neurobiology 2015, 31:214–222
220 Brain rhythms and dynamic coordination
sharp-wave-phase and peak oscillatory-activity [71]. The ripples were synchronized to different phases of the sharp-wave or even appeared without any inducer signs. The variations in the inducer-generator activity were further confirmed in detailed spike-field coherence analyses. Moreover, the idea that such differences may be an expression of different network topologies and dynamics was supported by the examination of the MSA typically measured with NET-fMRI. The MSA showed subtype-specific modulations of responses in the neocortex and in the noradrenergic and serotonergic neuromodulatory centers (locus coeruleus, raphe) that are known to significantly modulate the hippocampal circuits [72,73,74].
Conflict of interest statement
The ‘classical’ ripples aligned to the peak of their SPWs and were associated with enhanced neocortical activation and stronger down regulation of LC and raphe. This result suggests that these episodes occur more often during sleep states [75,76]. In contrast, ripples occurring without noticeable inducer signs show weaker downregulation of neuromodulatory centers. These data indicate their potential involvement in active memoryrelated behaviors [40,41,69] or in decision-making under unexpected uncertainty-conditions [77]. Such functional differences may be due to different network organizations with different inducers or generators of SPW-R [78].
1.
The optimization of NET-fMRI will also requires rigorous classification and pattern recognition methods for an unconditional detection and identification of events. The classic approach relies on scrutiny of predefined frequency bands. However, this approach may be unable to capture relevant phenomena in the absence of prior studies. In an effort to address this issue, we recently introduced (Besserve et al., submitted for publication) a principled LFP data-analysis relying on the extraction of dynamic components with characteristic power spectra using the Itakura-Saito non-negative matrix factorization technique. We then detected transient events with dictionary learning algorithms. The methodology permits the precise detection and identification of known thalamocortical and hippocampal events. Importantly, these findings also reveal previously unknown types of coupling and new MSA patterns. These new patterns include the pre-activation of neocortical areas before delta-events and the lack of pre-activation during spindles. Finally, statistical learning and complex dynamic systems methodologies may eventually enable the characterization of local episodes based on the concurrently recorded MSA profiles at any given time. The multidimensionality of the fMRI data compensates for the poor (1– 0.5 s) sampling rate and offers robust probability density functions reporting the occurrence or sparsity of individual events. Current Opinion in Neurobiology 2015, 31:214–222
Nothing declared.
Acknowledgements I thank Michel Besserve, Juan Felipe Ramirez-Villegas, and Oxana Eschenko for reading the early draft of the manuscript and providing useful suggestions. This research was supported by the Max Planck Society. I apologize to those whose work we have not been able to cite for reasons of space.
References and recommended reading Papers of particular interest, published within the period of review, have been highlighted as: of special interest of outstanding interest Freeman WJ: Making sense of brain waves: the most baffling frontier in neuroscience. In Biocomputing. Edited by Parelus P, Principe J, Rajasekaran S. Kluwer; 2008:33-55.
2.
Deco G, Jirsa VK, Robinson PA, Breakspear M, Friston KJ: The dynamic brain: from spiking neurons to neural masses and cortical fields. PLoS Comput Biol 2008, 4. A good review discussing the computational models at different spacetime scales. The authors describe the benefits of modeling at microscopic, mesoscopic and macroscopic levels and discuss the value of the mean-field and related formulations of dynamics with essential and complementary roles as forward models that can be inverted given empirical data.
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Logothetis NK: What we can do and what we cannot do with fMRI. Nature 2008, 453:869-878.
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Buzsaki G: Large-scale recording of neuronal ensembles. Nat Neurosci 2004, 7:446-451.
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Friston KJ: Modalities, modes, and models in functional neuroimaging. Science 2009, 326:399-403.
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Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL: A default mode of brain function. Proc Natl Acad Sci U S A 2001, 98:676-682.
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Bullmore E, Sporns O: Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 2009, 10:186-198.
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Buzsaki G, Logothetis N, Singer W: Scaling brain size, keeping timing: evolutionary preservation of brain rhythms. Neuron 2013, 80:751-764.
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Buzsaki G: Theta oscillations in the hippocampus. Neuron 2002, 33:325-340.
10. Sirota A, Buzsa´ki G: Interaction between neocortical and hippocampal networks via slow oscillations. Thalamus Relat Syst 2005, 3:245-259. 11. Steriade M, Llinas RR: The functional states of the thalamus and the associated neuronal interplay. Physiol Rev 1988, 68: 649-742. 12. Bragin A, Jando G, Nadasdy Z, Hetke J, Wise K, Buzsaki G: Gamma (40–100 Hz) oscillation in the hippocampus of the behaving rat. J Neurosci 1995, 15:47-60. 13. Leopold DA, Murayama Y, Logothetis NK: Very slow activity fluctuations in monkey visual cortex: implications for functional brain imaging. Cereb Cortex 2003, 13:422-433. 14. Arieli A, Sterkin A, Grinvald A, Aertsen A: Dynamics of ongoing activity: explanation of the large variability in evoked cortical responses. Science 1996, 273:1868-1871. 15. Singer W, Gray CM: Visual feature integration and the temporal correlation hypothesis. Annu Rev Neurosci 1995, 18:555-586. 16. Fries P: A mechanism for cognitive dynamics: neuronal communication through neuronal coherence. Trends Cogn Sci 2005, 9:474-480. www.sciencedirect.com
Neural-Event-Triggered fMRI of large-scale neural networks Logothetis 221
17. Ermentrout GB, Kleinfeld D: Traveling electrical waves in cortex: insights from phase dynamics and speculation on a computational role. Neuron 2001, 29:33-44. 18. Massimini M, Huber R, Ferrarelli F, Tononi G: Sleep slow oscillations as traveling waves: origins and pathways of propagation in humans. Sleep 2004, 27:32. 19. Steriade M, Nunez A, Amzica F: A novel slow (
ScienceDirect Neural-Event-Triggered fMRI of large-scale neural networks Nikos K Logothetis Brains are dynamic systems, consisting of huge number of massively interconnected elementary components. The activity of these components results in an initial conditionsensitive evolution of network states through highly nonlinear, probabilistic interactions. The dynamics of such systems cannot be described merely by studying the behavior of their components; instead their study benefits from employing multimodal methods. Neural-EventTriggered (NET) fMRI is a novel method allowing identification of events that can be used to examine multistructure activity in the brain. First results offered insights into the networks that might be involved in memory consolidation. On-going work examines the physiological underpinnings of the up and down modulation of metabolic activity, mapped with this methodology. Addresses Max Planck Institute for Biological Cybernetics, Imaging Science and Biomedical Engineering, University of Manchester, Manchester, United Kingdom Corresponding author: Logothetis, Nikos K ([email protected])
Current Opinion in Neurobiology 2015, 31:214–222 This review comes from a themed issue on Brain rhythms and dynamic coordination Edited by Gyo¨rgy Buzsa´ki and Walter Freeman
http://dx.doi.org/10.1016/j.conb.2014.11.009 0959-4388/# 2014 Published by Elsevier Ltd.
Introduction Neural activity patterns related to behavior occur at many different spatiotemporal scales and on different levels, ranging from molecules, genes and cells to neuronal microcircuits and large-scale neuronal networks that often involve distinct brain nuclei and regions. A computationally useful description of the hierarchical levels of organization considers the degree of covariance between neurons and the population size and extent of local neighborhoods [1,2]. The microscopic and mesoscopic levels predominantly refer to spiking and integrative dendritic activity, respectively. Macroscopic activity informs us about whole brain dynamics and interactions Current Opinion in Neurobiology 2015, 31:214–222
between large-scale neural systems, such as structures, nuclei and cortical regions. A predominant methodology for studies at the microscopic and mesoscopic-levels involves the use of single or multiple unit recordings. In these recordings, neuronal activity is often compared with sensory processing or behavior [3]. The development and optimization of electrode arrays and various intracranial imaging methods now permits recordings from an increasingly greater number of neurons, which provides information on larger neuronal ensembles [4]. In addition to microscopic and mesoscopic measurements, non-invasive brain imaging methods, such as electroencephalography (EEG), magnetoencephalography (MEG), positron emission tomography (PET) and functional magnetic resonance imaging (fMRI), are also used. These methods can be used in combination with biophysical modeling and multivariate pattern classification and decoding techniques to understand large-scale connectivity and the interactions of various neural structures and areas constituting the brain macroscopic system [5]. Improving our understanding of the function of neurons and microcircuits is important. Furthermore, understanding the anatomical and functional connectivity of large-scale networks is critical. The study of the brain’s complexity would also benefit from developing and employing methods combining simultaneous small and large-scale recordings of neural activity. Brains are complex dynamic systems par excellence. The brain system consists of many massively interconnected elementary components. The activity of these components results in an initial condition-sensitive evolution of network states through highly non-linear, probabilistic interactions. The dynamics of such systems cannot be described merely by studying the behavior of their elementary components. The system reflects a synergy-induced emergence of global patterns of neural activity. The study of these patterns requires the simultaneous acquisition of microscopic information at many different sites of brain networks. A spatial high resolution functional MRI scan with parallel imaging based on phased arrays of radiofrequency coils and reconstruction algorithms for acceleration volumes of 22 slices with 128 128 pixel in-plane resolution can be acquired with a temporal resolution of 0.5–1 s. It would undoubtedly be better to have 360 448 microelectrodes in a virtual three-dimensional grid within the brain rather www.sciencedirect.com
Neural-Event-Triggered fMRI of large-scale neural networks Logothetis 215
than tracking the metabolic changes in each voxel. However, this advance is unlikely to be achieved in the near future. The relationship between voxel-intensities and local field potentials is complicated and far from proportionate. Thus, the images require very careful interpretation [3]. The highly multidimensional information provided by fMRI may eventually provide useful constraints in the interpretation of brain states related to simultaneously acquired physiological data. These constraints can also be used in subsequent electrophysiological studies for an informed selection of brain regions in multisite recordings. This review briefly summarizes several characteristic indicators of brain-network selforganization and subsequently describes a first attempt to use so-called Neural-Event-Triggered fMRI (NETfMRI) for gaining insights into the brain states associated with various spontaneous neural events in different brain structures. Lastly, I briefly discuss problems with the interpretation of MR imaging data and the future perspectives of the NET-fMRI methodology.
Self-organized brain activity Over the last decade, the focus of neuroimaging studies has shifted from functional localization to functional integration and brain connectivity [5]. The seminal studies of Raichle and colleagues on the default mode network [6] provided analyses of data from resting-state fMRI. These studies demonstrated the existence of various types of statistical dependencies between separated brain regions that reflect intrinsic functional connectivity. Structural and functional MRI data have been analyzed with increasingly rigorous and refined approaches of statistical mathematics and theoretical physics, including graph-theoretical methods and modeling in the framework of complex dynamic systems [2,7]. Systems neuroscience has a long history of electrophysiological investigations of spontaneous microscopic and mesoscopic neural activity in humans and animals. A characteristic emerging property in many brain networks is oscillatory activity [1,8]. This activity has been studied in great detail in various systems, including the thalamocortical system, basal ganglia, hippocampal formation and the brainstem. Rhythmic activity often reflects the interactions of populations of neurons [9,10]. However, in the case of the thalamocortical networks, it may also be generated by single neurons as a result of interplay between specific intrinsic currents [11]. The oscillations may occur in the same brain state and interact with each other either within the same or across different brain regions. These interactions are commonly hierarchical, and the phase of slower oscillations often modulates the power of the faster oscillations [12,13]. The spontaneous activity patterns largely emerge from the very same networks underlying various behaviors. Therefore, it is not surprising that on-going activity often constrains or determines responses to sensory stimulation [14]. Moreover, www.sciencedirect.com
frequency-band selective modulation [15] and large-scale synchronization [16] of rhythmic activity are believed to play a crucial role in cognitive processing. Brain oscillations are similar to most biological rhythms and are weakly chaotic. Their frequency range extends from infraslow (300 mV) LFP deflections. The first detailed description PGO came from electrophysiological recordings in pons, LGN and the cortex of cats [35,36]. PGO waves are related to several important brain functions including sensorimotor integration, dreaming, development and learning [37]. Their relation to large-scale networks has been recently demonstrated in human fMRI studies reporting REM-related activations in the pontine tegmentum, ventro-posterior thalamus and the primary visual cortex in the absence of any visual input [38].
Spontaneous events and multi-structureactivity (MSA) profiles Decades of experimental work in animals and humans suggest that the aforementioned oscillatory patterns, inCurrent Opinion in Neurobiology 2015, 31:214–222
cluding single or multiple cycle short-lasting episodes, reflect state changes of self-organizing large-scale networks. These patterns regulate cognitive capacities, such as learning, memory encoding and consolidation and memory-guided decision making [39,40,41,42]. It is known that reactivation of neuronal ensembles that were active during awake experiences occur primarily during ripples [43–46]. Thus, the number of ripples increases after learning and the increase appears to predict memory recall both in rats [47,48] and in humans [49]. Conversely, the elimination of ripples by the electrical stimulation of HP during the post-learning SWS interferes with memory consolidation [50,51]. Similarly, disruption of PGO and REM-sleep appears to selectively interfere with the retention of procedural knowledge [52]. Evidently, the exact topologies of such networks and the emerging dynamic activity patterns resulting from their evolution over time are likely to be excellent indicators of specific brain functions and dysfunctions. However, electrophysiological recordings of neural events commonly involve one or more brain regions chosen by means of their anatomical connectivity patterns or by formerly established cooperative interactions of structures in the context of behavior. The global states associated with events remain elusive because of the dearth of methodologies permitting concurrent local recordings and wholebrain activity mapping. In an attempt to study the topology and dynamics of networks associated with events such as ripples, spindles or PGO waves, we have recently developed and applied so-called neural event triggered functional magnetic resonance imaging (NET-fMRI). This method permits concurrent multi-structure and multi-site intracranial recordings and the fMRI of whole-brain activity in 60– 70 regions of interest [ROI] corresponding to various cortical areas and subcortical structures or nuclei. These recordings can be performed in anesthetized and awake monkeys [53]. Figure 1 displays the ripple-triggered whole-brain activity map, the average time course of each ROI and the fraction of its voxels that were significantly up or down modulated within a peri-event time window. The fractional modulated volume and response-dynamics of each ROI, here termed as multi-structure-activity (MSA), offers some insights into the state of the brain networks associated with an event such as SPW-R. Sorting ROIs according to the developmental categorization of brain structures (e.g., metencephalon, mesencephalon, diencephalon, limbic and neo-cortex) revealed a modulation dichotomy between cortical and subcortical areas. SPW-Rs were consistently associated with robust activations of the neocortex and limbic cortex that took place concurrently with robust negative BOLD responses (NBR) in subcortical thalamic, associational (e.g., basal ganglia, cerebellum) and midbrain-brainstem neuromodulatory structures. Such downregulation of activity www.sciencedirect.com
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Figure 1
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Ripple-Triggered MSA. (a) Time–frequency representation of the SPW-R events. (b) Average activation maps from all of the anesthetized-monkey sessions. PBR is observed in the neocortex and limbic cortex, while activity suppression is seen in the diencephalon, mesencephalon and metencephalon. (c) Time courses of each group ROI. Note the sign change in the transition from cortical to subcortical areas and the differences in response onset. (d) Fractions of activated voxels for each group ROI.
in many different structures at the time of SPW-R occurrence may be due to a state-transition favoring optimal hippocampal–cortical communication. The thalamic NBR might indicate an optimal brain-state during which the signal-to-noise ratio of hippocampal– cortical communication is maximized by minimizing neural activity related to sensory processing. The strong downregulation of metabolic activity was also observed in large portions of associational subcortical brain www.sciencedirect.com
structures, such as the basal ganglia (BG), which are related to the acquisition of skills and habits. BGsuppression likely involves a complex interplay of excitatory and inhibitory afferents, such as the medial temporal and pallido-thalamocortical projections to the prefrontal cortex or the subicular projections to the striatum [54]. In addition, activity suppression in BG may be due to diverse neuromodulatory regulation mechanisms, such as the differential dopaminergic (SN/VTA) or amygdala regulation of hippocampal and Current Opinion in Neurobiology 2015, 31:214–222
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striatal activities [55,56]. Similarly, the deactivation of the pontine nuclei and cerebellar cortex could result from excitatory cortico-pontine or inhibitory subcortical projections onto specific subsets of GABAergic or glutamatergic pontine neurons, respectively [57]. An occasional concomitant activation (rather than deactivation) of the deep cerebellar nuclei could result from the deactivation of the inhibitory cortico-nuclear projections of the Purkinje neurons [57] and excitatory projections from various subcortical nuclei linked to the declarative memory system. All of these processes could optimize declarative memory consolidation by potentially reducing interfering activity related to other types of memory. Several studies have reported competition between learning and memory systems during the execution of various learning tasks [58,59]. There is evidence of post-learning antagonistic
interactions between memory systems in animal lesion and hippocampal inactivation studies [60]. The NETfMRI findings indicate that competition between memory systems may take place at the time of SPW-R occurrence during off-line states. The strong NBR of pons, LGN and foveal V1 activity during SPW-Rs occur despite the overall positive BOLD responses in the other primary sensory and associational cortices. These responses may be related to suppression of cholinergic neurons in the brainstem, including those inducing the PGO waves during the hippocampal–cortical dialog. REM and PGO are both related to procedural learning and synaptic consolidation. The network states related to synaptic and system consolidation may have synergistic or antagonistic interactions depending on learning/encoding phase, short-term retention of information, or long-term consolidation. The NET-fMRI
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MUA and BOLD Responses in Hippocampus (left panels) and the dorsal Lateral Geniculate Nucleus (right panels). Blue (62%) and dark-red (38%) traces are the means of two subsets of ripple-triggered neural or fMRI activity obtained by clustering the dLGN responses. (a) Ripple-triggered MUA responses in the pyramidal cell layer of the hippocampus. (b) MUA in dLGN. (c) BOLD average BOLD response of the HP-ROI, and (d) BOLD response of the dLGN-ROI. Insets show the BOLD modulation and electrode position for each structure.
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results offer some first global-hints and any serious explanation of the underlying physiological process will require further research.
Interpretation of the up- and down-regulation of metabolic activity The NET-fMRI results raise the question of correct interpretation of fMRI signals, which correlate with changes in metabolism rather than neural activity. MR signal interpretation is more complicated during spontaneous activity. Oscillations and synchrony could, in principle, reduce the metabolic requirements by increasing the synaptic efficiency. However, the oscillations and the gamma rhythm are associated with high-energy demands that require high oxidative energy metabolism, strong mitochondrial performance, and sufficient supply with oxygen and nutrients [61,62]. In general, the variance of positive BOLD responses (PBR) in the cortex is best explained by the dynamics of different band-limited-power signals derived from the LFP in anesthetized or drug-free animals. The interpretation of the LFP itself may also require caution because it reflects both sensory input and neuromodulation-induced changes in the local excitation-inhibition balance [63–65]. However, the relationship of neuronal inhibition and BOLD responses is not straightforward. The inhibition may increase or decrease energy consumption depending on the extent of local interneuron involvement [3]. Certain interneuronal classes directly control blood flow regulation [66], and the hemodynamic mechanisms of NBR are different from those of PBR [67]. Nonetheless, NBR itself may be seen as a reasonable marker of reduction of population-activity because it is often correlated with decreases in multiunit activity (MUA) [3,68]. On-going experiments in our laboratory with combined hippocampal, thalamic, cortical recordings and MR imaging show a robust correlation between up/down modulations and changes in MUA activity (Figure 2). Figure 2a,b shows ripple-triggered multiunit activity in the hippocampus and lateral geniculate nucleus, respectively. The responses in both structures were subdivided into two groups by clustering. Blue and dark-red traces show responses occurring in 62% and 38% of ripple-episodes, respectively. Figure 2c,d shows the corresponding BOLD responses. In both subtypes, there is a robust reduction in thalamic MUA, which fully supports the structure’s NBR. At first glance, this finding appears to be in disagreement with a large number of observations, suggesting certain degrees of synchrony between hippocampal ripples and thalamocortical spindles [10]. However, the slow MUA reduction often occurs with a brief, ripple-synchronized increase in MUA (see dark-red trace in Figure 1b). Therefore, we hypothesize that a plasticity-related activity enhancement of case selective neuronal subwww.sciencedirect.com
populations may occur concurrently with an overall decrease in population spiking. This strategy would increase the SNR of targeted processes. The decreases in MUA were also observed in some other brain regions with characteristic NBR, including the primary visual cortex, associational thalamus and pontine region. Cumulatively, these observations suggest that in most NBR areas revealed with NET-fMRI a reduction of MUA is highly probable and is potentially associated with diverse short-lasting activity changes at the time of SPW-R.
Conclusions and perspectives NET-fMRI is a new method that allows events to be detected, identified and used to examine MSA in the entire brain. Its application may prove highly informative even if the initial results are suggestive rather than decisive regarding the comprehension of network topology and dynamics. It should be noted that neither the activity maps nor the sequences of up and down-regulation indicate a causal relationship between the trigger event and the network activity changes. The state of widespread networks likely depends on a large number of variables. A subset of these variables may be gradually characterized following intensive future experimentation. Interestingly, large-scale observations may improve our understanding of the detailed nature of single neural episodes. SPW-R events are associated with the strong activation of the entire cortex. Is each event correlated with such extensive upregulation? In principle, global activation may simply be the result of averaging hundreds of events that individually have specific cortical targets. Alternatively, events may have different subtypes that each reflect different hippocampal networks and are associated with different MSA and different cognitive functions. SPW-R events have mainly been associated with memory consolidation. However, recent experimental evidence suggests that they are involved in other cognitive capacities, such as memory-driven decision making and working memory [41,69,70]. Such functions may be mediated by different networks and neuronal types. The connectivity patterns and dynamics of these networks could potentially influence the time-frequency characteristics of SPW-R. As mentioned above, SPWRs emerge from the interactions of CA3 and CA1 neurons. The synchronized bursting of CA3 neurons (inducers) yields a large LFP depolarization in the CA1 dendritic fields. The resulting interaction of excitatory–inhibitory cells in CA1 (generators) produces fast oscillation. The effects of inducers and generators are evident in the time– amplitude–frequency characteristics of sharp waves and ripples, respectively. Quantitative analysis of the NET-fMRI data revealed four subtypes of SPW-Rs differing in the relationship of Current Opinion in Neurobiology 2015, 31:214–222
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sharp-wave-phase and peak oscillatory-activity [71]. The ripples were synchronized to different phases of the sharp-wave or even appeared without any inducer signs. The variations in the inducer-generator activity were further confirmed in detailed spike-field coherence analyses. Moreover, the idea that such differences may be an expression of different network topologies and dynamics was supported by the examination of the MSA typically measured with NET-fMRI. The MSA showed subtype-specific modulations of responses in the neocortex and in the noradrenergic and serotonergic neuromodulatory centers (locus coeruleus, raphe) that are known to significantly modulate the hippocampal circuits [72,73,74].
Conflict of interest statement
The ‘classical’ ripples aligned to the peak of their SPWs and were associated with enhanced neocortical activation and stronger down regulation of LC and raphe. This result suggests that these episodes occur more often during sleep states [75,76]. In contrast, ripples occurring without noticeable inducer signs show weaker downregulation of neuromodulatory centers. These data indicate their potential involvement in active memoryrelated behaviors [40,41,69] or in decision-making under unexpected uncertainty-conditions [77]. Such functional differences may be due to different network organizations with different inducers or generators of SPW-R [78].
1.
The optimization of NET-fMRI will also requires rigorous classification and pattern recognition methods for an unconditional detection and identification of events. The classic approach relies on scrutiny of predefined frequency bands. However, this approach may be unable to capture relevant phenomena in the absence of prior studies. In an effort to address this issue, we recently introduced (Besserve et al., submitted for publication) a principled LFP data-analysis relying on the extraction of dynamic components with characteristic power spectra using the Itakura-Saito non-negative matrix factorization technique. We then detected transient events with dictionary learning algorithms. The methodology permits the precise detection and identification of known thalamocortical and hippocampal events. Importantly, these findings also reveal previously unknown types of coupling and new MSA patterns. These new patterns include the pre-activation of neocortical areas before delta-events and the lack of pre-activation during spindles. Finally, statistical learning and complex dynamic systems methodologies may eventually enable the characterization of local episodes based on the concurrently recorded MSA profiles at any given time. The multidimensionality of the fMRI data compensates for the poor (1– 0.5 s) sampling rate and offers robust probability density functions reporting the occurrence or sparsity of individual events. Current Opinion in Neurobiology 2015, 31:214–222
Nothing declared.
Acknowledgements I thank Michel Besserve, Juan Felipe Ramirez-Villegas, and Oxana Eschenko for reading the early draft of the manuscript and providing useful suggestions. This research was supported by the Max Planck Society. I apologize to those whose work we have not been able to cite for reasons of space.
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