Seizure prediction and intervention

Journal Pre-proof Seizure prediction and intervention

Christian Meisel, Tobias Loddenkemper PII:

S0028-3908(19)30469-1

DOI:

https://doi.org/10.1016/j.neuropharm.2019.107898

Reference:

NP 107898

To appear in:

Neuropharmacology

Received Date:

01 August 2019

Accepted Date:

30 November 2019

Please cite this article as: Christian Meisel, Tobias Loddenkemper, Seizure prediction and intervention, Neuropharmacology (2019), https://doi.org/10.1016/j.neuropharm.2019.107898

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Journal Pre-proof Seizure prediction and intervention Christian Meisel*, Tobias Loddenkemper Boston Children’s Hospital, Boston, MA, USA * corresponding author, email: [email protected]

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Journal Pre-proof Abstract Epilepsy treatment is challenging due to a lack of essential diagnostic tools, including methods for reliable seizure detection in the ambulatory setting, to assess seizure risk over time and to monitor treatment efficacy. This lack of objective diagnostics constitutes a significant barrier to better treatments, raises methodological concerns about the antiseizure medication evaluation process and, to patients, is a main issue contributing to the disease burden. Recent years have seen rapid progress towards better diagnostics that meet these needs of epilepsy patients and clinicians. Availability of comprehensive data and the rise of more powerful computational analysis methods have driven progress in this area. Here, we provide an overview on data- and theory-driven approaches aimed at identifying methods to reliably detect and forecast seizures as well as to monitor brain excitability and treatment efficacy in epilepsy. We provide a particular account on neural criticality, the hypothesis that cortical networks may be poised in a critical state at the boundary between different types of dynamics, and discuss its role in informing diagnostics to track cortex excitability and seizure risk in recent experiments. With the further expansion of digitalization in medicine, tele-medicine and long-term, ambulatory monitoring, these computationally based methods may to gain more relevance in epilepsy.

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Journal Pre-proof Lack of objective diagnostics is a barrier to more refined treatments Despite continued research efforts and many new treatments entering the market over the last decades, adequate seizure control remains difficult to achieve for 30% of pharmacologicallyrefractory patients (1–4). Therefore, novel treatment approaches that reduce seizure severity and frequency while limiting and eliminating adverse treatment effects are highly desirable for drugresponding and drug-refractory epilepsy patients (5, 6). In addition to the lack of appropriate treatments, there is also a remarkable gap in robust diagnostics to objectively characterize and monitor epilepsy and its treatment over time. This deficit of diagnostic tools is arguably one of the leading causes for the lack of more refined treatments. Specifically, the inability to detect seizures reliably, track seizure risk and monitor antiseizure medication (ASM) action and effectiveness constitute a significant barrier to improved and more personalized treatments in epilepsy. Urgent need for reliable seizure detection methods In current clinical practice, patient-reported seizure diaries are the mainstay of seizure frequency documentation, highlighting the importance of reliable assessments as seizure diaries remain the most important source for treatment adjustments and interventions. Specifically, routine clinical decisions regarding the choice of ASMs and their titration rely largely on the number and severity of reported seizures. Inaccurate seizure counts may lead to imprecise evaluation of treatment efficacy and impact treatment decisions. Accurate seizure counts are crucial due to the importance in evaluating effectiveness and efficacy of newly developed ASMs. Currently, seizure detection and counting in every-day settings rely largely on patient self-reports. Recent research, however, has demonstrated that these self-reports are highly unreliable: self-reported seizures correlate only weakly with seizures detected by electrographic activity (7), and self-reporting may be confounded either by impaired awareness during and after seizures (112). The inaccuracy of self-reports poses a significant dilemma for clinicians, patients and caretakers alike, to determine the best joint treatment approach, and also raises methodological concerns about the ASM evaluation process, as current medication efficacy evaluation and FDA approval hinge on self-reported seizure frequency and tentative reduction. Furthermore, objective seizure detection methods could be

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Journal Pre-proof designed to alert caregivers and thereby help provide sooner treatment to prolonged seizures, or ideally attempt to reduce or prevent prolonged seizures, and related complications, such as sudden unexpected death in epilepsy (SUDEP), which occurs mainly during or shortly after a seizure. Of note, a recent Cochrane review highlighted (nocturnal) supervision as the only way to prevent SUDEP and seizure related injuries besides improving seizure control through treatment adjustment (8).

Need to gauge seizure risk and susceptibility The inability to assess seizure risk and susceptibility – or, to put it another way, to forecast seizures – is a major clinical challenge and detriment to patient quality of life (9). In a recent comprehensive survey, patients voted the unpredictability of seizures as the top concern regardless of seizure frequency or severity, as evidenced by recent patient-initiated campaigns to solve this issue (10). Reliably gauging seizure risk could significantly improve quality of life by giving patients timely warnings of upcoming seizures and peace of mind when seizure risk if low. From a clinician’s perspective, robust seizure forecasting would drastically improve treatment efficacy. Accurate forecasting would also permit optimized drug dosing and timing to maximize drug effectiveness and minimize drug side effects (11, 12). The ability to forecast seizures could furthermore afford novel treatment approaches, such as timely, closed-loop interventions to avert impending seizures in the future (10). Examples of such an intervention could be the application of a fast-acting ASM, systemically or even at the seizure focus, or potentially electrical brain tissue stimulation (13, 14) that prevents the occurrence of the seizure (15–18).

Lack of cortical excitability measures in epilepsy management Apart from seizure detection and prediction methods, that could both facilitate refined treatments, epilepsy treatment currently also lacks diagnostics that provide objective, personalized assessments of the central physiological effects of ASMs. Increased levels of cortical excitability likely play an important role in the initiation and spread of epileptic seizures (19–22). Controlling

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Journal Pre-proof and reducing cortical excitability levels with ASMs consequently constitutes a first-line treatment in epilepsy (23). Remarkably, despite this prominent role of cortical excitability and ASM action to control it, there has been a lack of methods to assess brain excitability objectively, for example as a quantification method for efficacy of ASM treatment in people with epilepsy. Current methods to probe cortical excitability rely on brief, externally applied perturbations. Such perturbational approaches to test excitability by transcranial magnetic stimulation (TMS) or electrical currents have demonstrated great scientific value (24–28). For regular clinical use, however, these approaches are currently unsuitable due to their relatively complex designs, limitation in portability of related devices, and the lack of well-established absolute reference values. Currently, the relationship between cortical excitability, electrographic activity (e.g. interictal discharges and spikes), seizure propensity and how all of these are controlled (or not controlled) by ASMs is incompletely understood (106), as evidenced by some at times contradicting findings (107-109). While care has to be taken not to oversimplify these complex mechanisms, a clinical marker that measures the effectiveness of ASMs in reducing excitability levels from ongoing activity (without the need for complex-setup perturbations) (29) may be helpful to better untangle these relationships. Such a measure could potentially also help with the selection of the optimal combination and dosage of ASMs that sufficiently decrease excitability, while minimizing adverse drug effects, and further research is required to address this question.

Improved measures to quantify seizure occurrence, seizure risk and response to ASM treatment may help to enhance treatment options in epilepsy. The need for refined diagnostics to assess or predict treatment response to improve seizure control has long been acknowledged, e.g. in the National Institute of Neurological Disorders and Stroke (NINDS) published 2014 Benchmarks for Epilepsy Research (30). The aim of this review is to give an overview of recent developments in these domains. Progress has largely relied on three aspects, as will be outlined in later sections: the availability of large datasets, the use of refined data-driven analytics, and an improved insight into the dynamical mechanisms underlying epilepsy. We here focus on three key diagnostics

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Journal Pre-proof considered particularly influential: methods of seizure detection, seizure prediction and treatment. Some of the resulting diagnostic improvements have been the topic of recent comprehensive reviews focused either on seizure forecasting (31–35), seizure detection (112) or, more generally, machine or data-driven approaches to epilepsy (111). In contrast to these reviews, we here provide a survey of the field with particular emphasis on reviewing insights into the principles governing cortical dynamics and how these new insights may guide diagnostics development. These theorydriven approaches can be considered complementary to other, more data-driven approaches in epilepsy. Given the already existing comprehensive reviews on data-driven approaches, we will only provide a brief summary of what have been, in our opinion, the drivers of this progress, i.e. the availability of long-term data and advanced theory- and data-driven methods.

Long-term data, data-driven methods and dynamical insights as driving forces for refined diagnostics Recent decades and years have seen further steady progress towards better diagnostics that meet the needs of epilepsy patients and clinicians. Availability of large-scale data and quantitative, computational analysis methods has further driven progress (36). Recording and storage of physiology time series data, predominantly in the form of EEG, paired with quantitative approaches to understand the mechanisms and advance diagnosis and treatment options through application of mathematics, engineering and computational science has been an important aspect of epilepsy research over the last decades. By acknowledging the importance of ”big data” and related analytics, epilepsy research can be regarded a front runner in what is now increasingly realized also in other fields of medicine and may be called computational medicine (37). The epilepsy field has seen an increase in more comprehensive, long-term data bolstered by advances in data-driven approaches such as machine learning and dynamical insights into the underlying pathophysiological mechanisms giving rise to seizures, which have spurred further progress in establishing novel diagnostics.

Long-term data in epilepsy

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Journal Pre-proof The development of novel diagnostic methods for refined treatments may benefit from more comprehensive, detailed structural and neuronal activity data, to permit better insight into epilepsy pathomechanisms and allow validation of potential diagnostics. Prominent international research programs, including the Human Connectome Project and the BRAIN Initiative (Brain Research through Advancing Innovative Neurotechnologies) in the United States (38); the Human Brain Project in Europe (39); and the Brainnetome project in China (40) are currently developing some of the necessary technological tools for such large-scale clinical and basic research efforts. These programs also develop elaborate data recording techniques applicable to epilepsy. Stored in comprehensive repositories and following standardization, clinicians and scientists are increasingly able to access and utilize these large-scale data for further analysis (34, 41–44). Awareness that novel diagnostics related to seizure detection, prediction and therapy monitoring require continuous, annotated, long-term data, has resulted in several publicly available epilepsy databases. These databases include the EPILEPSIAE database (41), the iEEG.org database (44) and, as a more recent addition, the Epilepsy Ecosystem (45). The data contained in these databases consists of recordings from scalp and invasive EEG at a high sampling rate, with expert annotations of seizure occurrences and other clinical data. Beyond EEG, the use of big data approaches in epilepsy may also include other data sources, such as internet-based platforms. These platforms allow patients to report and track seizures (46). Long-term data recording was also essential in the first successful prospective seizure prediction trial (7). Tapping into these new long-term EEG datasets has already provided significant new insights, including the discovery of circadian and multidien seizure rhythms (11, 47) or circadian and sleep/wake cycle dependent changes in cortical excitability (29).

Wearable devices for epilepsy diagnostics To make epilepsy diagnostics available for broad clinical use, methods that build on non-invasive, easily recordable data streams are desirable (48). Specifically, the comparably complex setup and potential stigmatization that goes along with EEG could be a barrier to its broad applicability, and

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Journal Pre-proof intracranial devices requiring neurosurgical intervention may not be a good fit for all patients. Therefore, peripheral recording devices, such as wearables, may be of particular value for this purpose, since they afford continuous, non-invasive signal recording of multiple physiological parameters without an invasive placement procedure. At the same time, the compact design limits risk of stigmatization and may altogether increase patient adherence during long-term ambulatory use. Wristbands provide a feasible means of continuous and long-term monitoring of physiological signals, including electromyography, actigraphy, electrodermal activity, body temperature, and blood volume pressure. The close monitoring of autonomous nervous system parameters and movement (49) has supported novel methods to detection of generalized tonic clonic seizures (50). Several seizure detection devices have recently been FDA approved regarding their ability to detect generalized tonic-clonic seizures, and further devices are currently under investigation. These devices include one or several physiological parameter recordings, in addition to EEG, including heart rate, actigraphy, electromyography, or electrodermal activity, and video, sound, and movement sensors, among others, either as an isolated modality, or in combination. Once more prolonged signal recordings become available, not only detection of generalized tonic-clonic seizures, but also evaluation of other seizure types may become more feasible. Machine learning techniques applied to larger datasets may provide new insights into seizure patterns and occurrence, potentially facilitating improved epilepsy monitoring and management (51). Acquisition and analysis of comprehensive datasets hold promise to developing and validating novel diagnostics by providing a standard reference. However, this approach also poses dataanalytic challenges and requires analytic tools. These tools or approaches must be capable of sorting through the complexity of these comprehensive datasets. The analysis of these comprehensive data can thereby benefit from recent improvements in data analytical techniques. Generally, computational approaches in data science, as applicable to epilepsy and long-term datasets, may consist of two major classes: data-driven, theoretically agnostic methods, and theory-driven models that specify and incorporate relations between variables.

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Data-driven approaches towards diagnostics Data-driven approaches can use machine learning, i.e. algorithms that learn a task without being explicitly programmed for it (52, 111). Deep learning algorithms that use multiple processing layers to achieve better results have achieved recent performance advances in this field (53, 54). Only recently researchers applied deep learning also for diagnostic purposes in epilepsy, where seizure prediction was a predominant focus. For example, based on long-term invasive EEG data, deep learning algorithms using convolutional neural networks have shown better-than-chance performance in correctly classifying data as either pre- or interictal (55, 56). In a pseudoprospective approach, researchers have obtained such superior performance even when algorithms ran on a low-energy computer chip suitable for future wearable devices (57). The underlying presumption for these approaches is that a reliable detection of the preictal state is a prerequisite for seizure forecasting diagnostics. Albeit these and several other studies provided promising results, the development of algorithmic approaches to seizure prediction also brings about challenges how to assess performance in a statistically meaningful, standardized manner, and how to benchmark algorithms with each other. Seizure prediction competitions have emerged to address these needs, and to drive the field forward by standardizing the comparison of different algorithms on the basis of their performance with a common set of data. The two most recent competitions, the American Epilepsy Society Seizure Prediction Challenge (58), and the Melbourne University AES/MathWorks/NIH Seizure Prediction Contest (59), were aimed at finding the best algorithms to differentiating interictal from preictal episodes based on retrospective, long-term invasive EEG data. Both competitions attracted a large number of participants, for many of whom the competition was the first exposure to epilepsy research and demonstrated that seizure forecasting (better: the differentiation between pre- and interictal states) was possible with a better-than-random chance (32, 45). Importantly, these competitions have proven how crowdsourcing for best algorithms could become a practical approach, potentially not only for seizure forecasting but also for other diagnostics in epilepsy and

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Journal Pre-proof beyond. These large-scale competitions, platform and data ecosystems can help foster an openscience approach to computational diagnostics development by making data and source codes publicly available which will be a strong asset to further increasing scientific transparency and reproducibility. Addition of clinical epilepsy and seizure data, such as seizure type, seizure location and seizure timing, among other variables, may further improve seizure detection and prediction. Mounting evidence suggests seizure patterns based on clock timing, vigilance states, including wakefulness and sleep, but potentially also circadian or ultradian seizure patterns, may help to improve detection and prediction algorithms. Furthering knowledge of pre-existing related patterns may not only improve diagnostic and predictive capabilities but may also assist in closing the loop, by providing care interventions based on these results, such as treating at times of greatest seizure susceptibility. These treatment patterns, chronotherapy, are already in use in other medical disciplines and may lead to more personalized epilepsy and seizure care, with improved seizure control and fewer side effects (60).

Data-driven techniques like machine learning offer a powerful approach to develop novel diagnostics, such as for seizure forecasting. However, given the requirement for large datasets to train and validate algorithms, diagnostics that incorporate already existing knowledge may be desirable in many cases, as broader amounts of input data may limit the overall amount of data needed. Furthermore, data-driven approaches may be limited in providing information about the generative mechanisms underlying these data. For example, although deep learning may effectively discriminate when a patient is in a preictal state, it is currently not well suited to give novel insight into the underlying dynamical mechanisms. Knowledge of these mechanisms, however, could be valuable to inform future therapies or diagnostic markers. In contrast, theory-driven approaches have the potential to both improve clinical practice and shed light on the generative mechanisms underlying these datasets. This is because theory-driven approaches incorporate models — that is, information about system function — into their algorithms. Novel and plentiful datasets can help to verify or falsify these models and, eventually,

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Journal Pre-proof refine initial prototypes for better diagnostics. Clearly, data-driven methods like machine learning and theory-driven methods are not mutually exclusive and can benefit from each other. For example, theory-informed models can identify relevant features in datasets to be used in machine learning. Theory-driven approaches may thus, by incorporating prior knowledge in their models, massively reduce the parameter space in data-driven methods and improve their performance. Thus, despite the power of machine learning, developing adequate theoretical frameworks (or theory-driven approaches) is still important for epilepsy research.

Examples of theory-driven approaches towards diagnostics In epilepsy, advancing insights into the underlying mechanisms may lead to a better understanding of ictogenesis, better translation into methods for the detection, prediction and control of seizures, and will eventually, based on better diagnostics, afford improved treatments. Computational modeling has a long history in epilepsy research (61, 62). In the context of this review, we survey recent advances based on dynamical systems theory related to the general understanding of normal cortical functioning (63). A central hypothesis in this setting, has been that cortical network dynamics reside near a critical state at a boundary between distinct types of dynamics (64). Only recently researchers have linked this body of literature to questions related to the understanding of cortical dysfunction in diseases like epilepsy. Here, we offer a brief introduction to this framework and review recent efforts to find diagnostics related to ictogenesis and monitoring of ASM action based on this framework. Dynamical systems theory is a field at the intersection between physics and mathematics used to describe the long-term behavior of systems in a variety of disciplines (65). The concept arose from the general observation that, for many realworld systems, especially those composed of many parts, it can be difficult to find the precise solutions to the mathematical equations defining every part and their interactions. Quite often, however, such detailed information is not the information that clinicians and researchers seek. Instead, the long-term behavior of the system as a whole is more informative. Research questions for dynamical systems theory include whether it will settle into a steady state, and what those

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Journal Pre-proof possible steady states are. The dynamical systems approach can describe the behavior of neural systems along different scales. For example, dynamical systems theory was essential for developing an understanding of the electrical and dynamical properties of individual neurons (66– 69). The pertinence of dynamical system frameworks extends beyond individual neurons and has contributed to the understanding of cortical network function and dysfunction with implications for epilepsy. Below, we discuss implications for epilepsy monitoring, seizure prediction and detection. To offer a brief introduction, we will begin by considering a simple cortical network model. In physics, highly simplified models have proven useful to distill the essence of a phenomenon before investigating how further details present in the real-world system reshape this essence. We will discuss insights into the mechanisms governing cortical excitability and monitoring of excitability levels in epilepsy patients. We then review precursors of rapid state transitions dictated by dynamical systems theory and how they may relate to early warning signs of epileptic seizures. We point out several current questions and connections to other phenomena. Because of the emerging connections, e.g. between network cortex network structure and (dys-)function, we believe that this framework inspires discussions and the development of tools that could lead to useful diagnostic tools in epilepsy independent of the validity of the framework itself.

Criticality as a fundamental property of cortical network dynamics Envision a large directed network of excitable nodes where each node has outgoing links that can propagate activity to other nodes. For the nodes one may consider cortical regions or a crude model of neurons where links correspond to synapses connecting pre- and postsynaptic nodes. In this model, activity propagates forward across layers when connection strengths are sufficiently strong. Clearly, this model is overly simplistic and omits many other details present in the nervous systems. However, this relatively simple model has all ingredients to show properties, in particular a critical phase transition separating two dynamical regimes, that are similarly found in much more complex models with realistic ingredients in terms of its physiological details (70, 71).

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Journal Pre-proof Consequently, researchers have used similar simple models to successfully predict the dynamics of tissue from the cortex in humans, non-human primates, rats, and turtles (72–80). To understand the macroscopic dynamics of the system, envision the spreading of activity as a function of the network’s connectivity. Here, connectivity refers best to effective connectivity that stands for causal relationships between distinct regions or neurons (81). The average proportion of activated nodes defines network activity. When connectivity is low, i.e. one node on average excites less than one postsynaptic node, activity will stay small and not exploit the whole network (Fig. 1 A left). In contrast, if connectivity is too high, i.e. one node on average excites more than one postsynaptic node, activity will always excite the whole network (Fig. 1 A right). To prevent these two extremes, one must carefully tune connectivity to afford balanced propagation that prevents premature die-out and runaway excitation (Fig. 1 A middle). In physics, one defines an ’order parameter’ to distinguish different macroscopic behaviors of the system, which here corresponds to the average activity of the network. Upon variation of an ambient property, called the ’control parameter’, here the effective connectivity, it is possible to investigate how this order parameter changes. When plotting the level of activity as a function of the connectivity, one observes a characteristic phase diagram with a subcritical quiescent phase and a supercritical active phase (Fig. 1 B) (64, 82). A phase (or critical) transition marks the boundary between these two phases. The critical transition marks the point where the order parameter undergoes a sharp turn. The mathematical analog of phase transitions are bifurcations. While, at the transition, the model under consideration exhibits the onset of activity that percolates through the entire network, other types of phase transitions or bifurcations are possible, such as the onset of synchronization in neural activity (83, 84). Several lines of research suggest that these concepts are also relevant for biological cortical network function. First, robustness of occurrence of phase transitions in models of cortex dynamics (70–80) suggests that phase transitions are likely also a generic feature in biological networks. This presumption is supported by empirical evidence from reduced slice preparations, where effective connectivity can be pharmacologically in- or decreased, and where dynamics closely

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Journal Pre-proof match these model

predictions (72,

83). Second, in order to

avoid the

extremes, i.e. quiescent

and active phase,

normal network activity

under

physiological conditions

should be poised

near the critical

transition between

the two phases. The

hypothesis that

normal cortex activity

resides at or near

criticality is supported

by an increasing

number of empirical

studies

demonstrating cortical activity to exhibit hallmarks of criticality in vivo (80, 85–88). Third, the hypothesis that cortical networks operate at or near a critical state is furthermore attractive because criticality is known to bring about optimal information processing and computational capabilities (73, 74, 78, 89, 90). Being at or near criticality could thus be an evolutionary advantage for brain networks (91). Conversely, cortex dynamics may deviate from criticality during focal seizures (92) which may explain the impaired functioning and information processing in affected networks during seizures. Collectively, these theoretical and experimental results and arguments make a strong case that criticality, controlled by effective connectivity, could also play a role in human cortical networks. Signatures of criticality have been observed in cortical networks at various spatial scales ranging from single-cell resolution networks (88) to large-scale networks monitored by EEG and magnetoencephalography (MEG; 85, 86, 92) which further supports the relevance of this framework, also for clinical questions. While the relevance of criticality in human cortical networks is still a hypothesis, framework may inspire discussions and may provide guidance on diagnostic tools in epilepsy. The confirmation or rejection of these hypothetical tools can, by itself, be considered a test for the appropriateness of the above framework. Subsequently, we will discuss some specific implications for epilepsy diagnostics that arise from this framework.

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Figure 1. Growing evidence suggests that activity propagation in cortical networks is adequately described by a phase transition which may inform diagnostic measures to track excitability and treatment efficacy. A, Illustration of activity propagation in feedforward networks with low connectivity, where activity dies out prematurely (blue, left), and high connectivity, where activity exhibits run-away excitation (red, right). Balanced activity propagation is achieved at moderate connectivity levels (grey, middle). B, Network activity exhibits a phase transition between an inactive (subcritical, blue) and an active (supercritical, red) regime controlled by effective connectivity. C, Schematic illustration how ASMs may shift the state of cortical networks to a different dynamical regime. While cortical network dynamics may normally be poised in the vicinity of the phase transition with moderate levels of connectivity (network state indicated by the black probability distribution), antiseizure medications (ASMs, AEDs) tend to shift dynamics further into the subcritical phase with lower connectivity (blue probability distribution; 29, 102).

Intrinsic excitability measures By linking cortical activity and effective connectivity, the above framework provides precise guidance on how to track cortical network excitability by passively monitoring collective activity in the brain. In line with predictions from computational models (70, 72–78, 92) and experiments on reduced cortex preparations (72, 83), recent work in epilepsy patients has indicated that monitoring of how activity spreads across cortex tracks cortical excitability levels (30, 93). Specifically, activity across different cortical sites was monitored by quantifying synchronization during intracranial recordings, and synchronization across cortical sites correlated with stimulation-evoked cortical potentials, a direct measure of cortical excitability. Additionally, this measure may help track the central physiological effects of ASMs over extended periods of time in epilepsy patients. ASMs generally shifted cortex dynamics towards lower synchrony/activity levels, in line with theoretical expectations of a more subcritical regime and increased distance to the phase transition (83) (Fig. 1 C). In contrast to externally applied perturbations to probe excitability, such as electrical

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Journal Pre-proof stimulation or transcranial magnetic stimulation (TMS), these intrinsic excitability measures (IEMs) were based on monitoring of ongoing activity. Being less invasive and complex in setup than TMS, this passive approach may potentially facilitate more personalized monitoring of ASM function and treatment in the future. As a diagnostic tool that is applicable to a larger number of patients, it will be important to establish similar reliable measures of excitability levels based on scalp EEG. Original studies used invasive EEG (or ECoG), which may not be indicated for many patients. Robust measures to quantify brain tissue excitability may improve treatment options for the one-third of epilepsy patients that are pharmacologically intractable. Closed-loop intervention systems (14), for example, might benefit from real-time feedback of excitability levels in order to prevent or reduce seizures by means of acute intervention, such as medication application and stimulation, in the future.

Critical slowing down as a potential diagnostic marker of network state Phase transition theory provides further markers that can be highly informative about the specific network state at any given time point. A general feature of systems near a critical phase transition is critical slowing down (94). Critical slowing down occurs because of the repeatedly slower recovery from small perturbations when a bifurcation or phase transition is approached (95). For illustration, consider a ball in a two-well potential (Fig. 2 A). The ball sits in one of the two wells initially. By slowly changing the potential landscape, the ball may eventually transition into the other well, which corresponds to the phase transition. Upon a small perturbation, for example by a small system fluctuation or variation, such as noise, leading to a kick, the ball will return to the bottom of the first well exponentially fast after some rolling left and right (Fig. 2, A right). As the transition to the other well approaches, this recovery from perturbations becomes progressively slower, and this is the essential principle of critical slowing down. By consequence, critical slowing down can be observed from data by measuring the recovery rate of system variables after small perturbations or by an increase in variance, as well as higher autocorrelation values as the transition is approached (96, 97). Research over the last years has provided the exact mathematical scaling laws

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Journal Pre-proof underlying critical slowing down (98, 99). With these scaling laws, the state of the system, i.e. how far it is from the critical point of phase transition, can in principle be determined (100). Of note, critical slowing down may only be observed when noise levels are moderate, i.e. when the system is not kicked into the other state by large noise and/or external influences or perturbations. In the context of our example model above, critical slowing down may be envisioned by considering a small perturbation as activation of a low number of neurons. In the subcritical, i.e. the inactive state, the activated neurons will activate only a smaller number of neurons, and therefore the signal dies out quickly (i.e. exponentially fast) over time. In the supercritical state, i.e. the active state, the activation of neurons will trigger a cascade that stays active for a long time. But due to the high connectivity and many neurons being activated regardless due to the ongoing selfsustained activity of the system, the perturbation-evoked response would also be small, and decrease exponentially fast in time. Near the critical transition between these two states, in contrast, cascades would be relatively long, with negligible background activity, so that the perturbation response would last longest in the system. Thus, critical slowing down should be observable in cortical networks. There are several implications of this systems theory that may translate to epilepsy monitoring and diagnostics. First, as sensitive variables to state changes, critical slowing down markers have been shown to track network states under changing levels of excitability (101, 102), and over the sleep/wake cycle (80, 103). Second, critical slowing down may potentially provide early warning of impending seizures in some patients. While seizures may not necessarily correspond to supercritical dynamics in the above model, it is possible that seizure onset is governed by a critical transition, at least in some patients, in which case critical slowing down has been predicted to occur (99). This prediction is supported by recent experiments in animal models of epilepsy (104) and long-term human data (105) where critical slowing down has been observed prior to seizures in some cases. The relevance of critical slowing down as an informative marker of network state and resilience to seizures is also indirectly supported by the successful seizure prediction performance in a recent data science competition (59) where critical slowing down features combined with deep neural networks were used (Fig. 2 B). Third, as consequence of correlations

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Journal Pre-proof becoming more long-range due to critical slowing down near the critical transition, it is possible that external perturbations or spontaneous fluctuations occurring in one part of the brain can affect other, more far away parts of the central nervous system. For instance, it is conceivable that that these long-range correlations could potentially allow assessment of the state by monitoring performance in a recent data science competition (58) where CSD features combined withbrain deep neural networks were used (Fig. 2 B). Third, as consequence of correlations becoming more long-

remote and indirectly connected physiological signals, such as modalities related to the autonomic range due to critical slowing down near the critical transition, it is possible that external perturbations or spontaneous fluctuations occurring in one part of the brain can affect other, more

nervous system (110).

far away parts of the central nervous system. For instance, these long-range correlations could

A

Position

Near transition

Time

B no seizure

Machine Learning

seizure soon

Figure 2. Critical slowing down as a sensitive marker of state changes in cortical network dynamics. A, Illustration of critical slowing down, i.e. a system’s tendency to recover more slowly from small perturbations upon approaching the state transition. B, Example of combining theoryand data-driven approaches in a recent data science competition (59). Measures of critical slowing down (red) are derived from long-term iEEG datasets (black) and used as features for machine/deep learning to classify pre- and interictal data segments.

Conclusion and Outlook 18

Journal Pre-proof The increasing availability of comprehensive physiology data along with computational methods has further stimulated research to develop novel epilepsy diagnostics to more reliably detect, classify and forecast seizures as well as to monitor treatment efficacy. Such diagnostics are urgently needed. Computational approaches towards this end can be broadly divided into theoryand data-driven ones, and both have provided significant advances over the recent years. With the further expansion of digitalization in medicine, tele-medicine and long-term, ambulatory monitoring, these automated, computationally based methods will likely gain even more relevance in epilepsy and other fields of medicine. These developments will not only require researchers to develop expertise in computational methods in order to deal with these large datasets. Equally importantly, they will also require clinicians to understand and familiarize themselves with the computational and data-scientific aspects of epilepsy in order to select and apply the best diagnostics and treatments for their patients in the future. Disclosure: This article was supported by the Epilepsy Research Fund. CM acknowledges support by the Brain & Behavior Research Foundation. Tobias Loddenkemper serves on the Council of the American Clinical Neurophysiology Society, on the American Board of Clinical Neurophysiology, as founder and consortium PI of the pediatric status epilepticus research group (pSERG), as an Associate Editor for Wyllie’s Treatment of Epilepsy 6th edition and 7th editions, and as a member of the NORSE Institute, PACS1 Foundation, and CCEMRC. He is part of patent applications to detect and predict clinical outcomes, and to manage, diagnose, and treat neurological conditions, epilepsy, and seizures. Dr. Loddenkemper is co-inventor of the TriVox Health technology, and Dr. Loddenkemper, and Boston Children’s Hospital might receive financial benefits from this technology in the form of compensation in the future. He receives research support from the Epilepsy Research Fund, NIH, the Pediatric Epilepsy Research Foundation, and received research grants from Lundbeck, Eisai, Upsher-Smith, Mallinckrodt, Sunovion, Sage, Empatica, and Pfizer, including past device donations from various companies, including Empatica, SmartWatch, and Neuro-electrics. In the past, he served as a consultant for Zogenix, Upsher Smith, Amzell, Engage, Elsevier, UCB, Grand Rounds, Advance Medical, and Sunovion. He performs video electroencephalogram long-term and ICU monitoring, electroencephalograms, and other electrophysiological studies at Boston Children's Hospital and affiliated hospitals and bills for these procedures and he evaluates pediatric neurology patients and bills for clinical care. He has received speaker honorariums from national societies including the AAN, AES and ACNS, and for grand rounds at various academic centers. His wife, Dr. Karen Stannard, is a pediatric neurologist and she performs video electroencephalogram long-term and ICU monitoring,

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Journal Pre-proof electroencephalograms, and other electrophysiological studies and bills for these procedures and she evaluates pediatric neurology patients and bills for clinical care.

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Journal Pre-proof 

Improved measures to quantify seizure occurrence, seizure risk and response to ASM treatment may help to enhance treatment options in epilepsy



The increasing availability of long-term, multi-modal data along with improved data- and theory-driven analytical approaches may support the development of such diagnostic measures



As a theory-driven framework, the criticality hypothesis posits that cortical network activity is poised at the boundary between distinct types of dynamics



We here offer a survey on recent data- and theory-driven efforts for seizure detection, forecasting and ASM monitoring with an emphasis on leveraging theory-driven insights for diagnostics development