Deep brain stimulation for neurodegenerative disease

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Deep brain stimulation for neurodegenerative disease: A computational blueprint using dynamic causal modeling Rosalyn Moran1 Virginia Tech Carilion Research Institute & Bradley Department of Electrical and Computer Engineering, Virginia Tech, Roanoke, VA, USA Department of Psychiatry & Behavioral Medicine, Virginia Tech Carilion School of Medicine, Roanoke, VA, USA 1 Corresponding author: Tel.: +15405262136; Fax: +540 985 3373, e-mail address: [email protected]

Abstract Advances in deep brain stimulation (DBS) therapeutics for neurological and psychiatric disorders represent a new clinical avenue that may potentially augment or adjunct traditional pharmacological approaches to disease treatment. Using modern molecular biology and genomics, pharmacological development proceeds through an albeit lengthy and expensive pipeline from candidate compound to preclinical and clinical trials. Such a pathway, however, is lacking in the field of neurostimulation, with developments arising from a selection of early sources and motivated by diverse fields including surgery and neuroscience. In this chapter, I propose that biophysical models of connected brain networks optimized using empirical neuroimaging data from patients and healthy controls can provide a principled computational pipeline for testing and developing neurostimulation interventions. Dynamic causal modeling (DCM) provides such a computational framework, serving as a method to test effective connectivity between and within regions of an active brain network. Importantly, the methodology links brain dynamics with behavior by directly assessing experimental task effects under different behavioral or cognitive sets. Therefore, healthy brain dynamics in circuits of interest can be defined mathematically with stimulation interventions in pathological counterparts simulated with the goal of restoring normal functionality. In this chapter, I outline the dynamic characterization of brain circuits using DCM and propose a blueprint for testing in silico, the effects of stimulation in neurodegenerative disorders affecting cognition. In particular, the models can be simulated to test whether neuroimaging correlates of nondiseased brain dynamics can be reinstantiated in a pathological setting using

Progress in Brain Research, ISSN 0079-6123, http://dx.doi.org/10.1016/bs.pbr.2015.07.002 © 2015 Elsevier B.V. All rights reserved.

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DBS. Thus, the key advantage of this framework is that distributed effects of DBS on neural circuitry and network connectivity can be predicted in silico. The chapter also includes a review of how DCM has been used to characterize the effects of DBS in Parkinson’s disease.

Keywords Dynamic causal modeling, Neurodegenerative disease, Deep brain stimulation, Neural mass models, Bayesian model inversion

1 INTRODUCTION With the announcement of the National Institute for Health’s SPARC (Stimulating Peripheral Activity to Relieve Conditions) initiative, the era of “electroceuticals” has been heralded. The SPARC initiative will support research into novel therapies that utilize closed-loop neuromodulation to modulate end-organ function (pancreas, lungs, etc.). Electroceuticals are thus a rapidly growing area of research and commercial interest (Famm et al., 2013; Geddes, 2014) with the promise to provide these new therapies for a wide range of diseases from cardiovascular disease to cancer (Birmingham et al., 2014). In light of this, several scientific commentaries have underscored the similar challenges that face the use of neuromodulation to offset neurological and psychiatric diseases as well as peripheral systems. Outside of hardware development, where considerable advances in the scale and power requirements of stimulation devices have already been made (Ewing et al., 2013; Lee et al., 2013), formidable challenges for neuromodulation in neurological and psychiatric diseases lie in understanding the circuitry that transmits critical electrical impulses and how these degrade in disease states. Hence, mapping the function of potential target pathways and their downstream consequences are a fundamental first step: “The goal is to build devices that target only the signal that elicits a desired effect, and not those that could alter functions in other parts of the body … It is a mammoth task … It’s like putting a device across a highway, and trying to figure out, by looking at the cars passing, which will get off at which exit.”, Dr. Brian Litt in Electroceuticals spark interest (Reardon, 2014). Neurostimulation devices have been successfully used to treat neurological disorders, particularly in movement disorders (Umemura et al., 2003) and more recently in epilepsy (Andrade et al., 2006). In complex psychiatric diseases, treatment using deep brain stimulation (DBS) also shows promise—for example, in ameliorating treatment-resistant depression (Mayberg et al., 2005) and obsessive compulsive disorder (Greenberg et al., 2006; Box 1).

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BOX 1 OUTLOOK OF ONGOING TRIALS FOR THE DEVELOPMENT OF DBS THERAPEUTICS IN EXEMPLAR PSYCHIATRIC DISORDER: TREATMENTRESISTANT EPILEPSY Treatment-resistant depression/major depressive disorder Medial forebrain bundle (NCT02046330)

11/1/2013–11/1/2017

Subcallosal cingulate gyrus (NCT01801319)

6/1/2011–6/1/2016

Subgenual white matter (NCT01331330)

5/1/2011–12/1/2014 2/1/2013–1/1/2015

Lateral habenula (NCT01798407) Subcallosal cingulate (NCT01898429)

7/1/2013–10/1/2016 5/1/2014–3/1/2018

Nucleus accumbens (NCT01973478) 2011

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Trials are timelined with electrode placement. Source: clinicaltrials.gov.

In terms of DBS target locations, clinical trials often target different brain regions. In the case of DBS for treatment-resistant depression, however, multiple research teams have converged on the subgenual cingulate (SCG) region (Box 1) for their stimulation zone. The rationale is that connections from SCG and orbitofrontal regions to subcortical striatal regions underlie DBS’s functional efficacy, and this hypothesis has been supported by detailed anatomical and functional connectivity analysis (Greicius et al., 2007; Johansen-Berg et al., 2008). In contrast to anatomical and functional connectivity, dynamic causal modeling (DCM) provides a characterization of effective connectivity, necessitating a circuit model of dynamic interactions among neurons (Friston, 2011). Measures of effective connectivity provide for contextual dependence in synaptic connections, e.g., through neuromodulation or task demands. Thus, one can measure connectivity changes under suitable experimental paradigms to probe a particular dysfunctional circuit. This connectivity assessment, and the potential to reverse network connectivity from pathological to nonpathological patterns, is the focus of this chapter. For example, in neurodegenerative disease such as Alzheimer’s disease (AD), a first task may be to define the “highway and exit ramps” or neural circuitry that underpins successful memory performance and compare these measures to disruptions in memory and executive function in AD. This could include collecting connectivity measures across brain circuits during different memory procedures such as encoding, maintenance, consolidation, or recall. DCMs are well suited for this characterization task as they allow for the assessment of effective connectivity and taskinduced modulations (Friston et al., 2003a), and can also incorporate structural

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tractography-based measures as priors on the functional architecture (Stephan et al., 2009). Since DCM estimates of brain connectivity can be correlated with behavior, specific components of a network or graph may be relevant in a clinical setting, i.e., one may first find that a particular connection must be beyond a certain threshold to support memory encoding. DCMs also bridge functional magnetic resonance imaging (fMRI) and electrophysiological (electroencephalographic and magnetoencephalographic) imaging modalities by offering conceptually identical goals (measures of effective connectivity), but with different generative models that provide coarse but whole brain (fMRI) or synaptically informed but spatially limited electroencephalography (EEG) measures of connectivity (Moran et al., 2008). In particular, the nonlinearities present in both types of generative models will serve as a critical component for assessing the effects of stimulation protocols, since stimulation effects can be tested for nonintended and ameliorative consequences—within the defined networks. For example, a 100-Hz stimulation protocol could induce firing properties at nonharmonic frequencies and compete with signal propagation in functionally necessary pathways. DCMs allow for the testing of these antagonistic effects, allowing for a more complete understanding of the mechanisms of action of stimulation. This chapter is designed to provide a detailed account of how multimodal neuroimaging and DCM can be used to test the effects and likely outcomes of DBS. Recent evidence suggests that the therapeutic efficacy of DBS can result from a multitude of mechanisms, for example, DBS protocols have been shown to promote synaptic plasticity in neurological disorders including Parkinson’s disease (PD) (Kumar et al., 2003) and tinnitus (Tass et al., 2012). These effects have been found to occur secondary to the direct, “online” neural circuit manipulations that augment signal progression through diseased cortical and subcortical networks. In PD, exaggerated synchrony in neuronal firing at beta-band frequencies (16–32 Hz) (Schnitzler and Gross, 2005) is reduced by high-frequency stimulation (HFS) of the subthalamic nucleus (STN) (Bronte-Stewart et al., 2009; Eusebio et al., 2011). HFS of the STN breaks synchronous firing with the motor cortex through antidromic connections (Li et al., 2012), promoting flexibility in basal ganglia output which alleviates motor symptoms of the disease (Kang and Lowery, 2014). These findings have emerged following FDA approval of DBS for PD and >70,000 implants in patients in the United States (Bronstein et al., 2011). In this chapter, an in silico computational paradigm is proposed so that potentially successful DBS outcomes can be tested for other disorders without the need for surgery. In particular, the chapter focuses on how to test whether DBS could ameliorate impairments in AD. The goal of the framework is to test whether DBS can be used to restore connectivity metrics to near healthy levels, rather than how to restore behavior per se. However, should the particular connection be associated with good performance in the healthy case, this first-pass assessment can be used to help determine which parts of a brain network are most important in terms of restored functionality. The rest of the chapter is arranged into five subsections comprising models and applications. In the first subsection, DCM for fMRI is explained with reference to how DBS effects on neural dynamics and behavior could be incorporated into

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new simulations on data-optimized models. In the second subsection, I describe DCM for cross-spectral densities (CSDs) and how those models may furnish more precise cellular targets using noninvasive EEG. This is followed by a description of how neurostimulation may be modeled. In the final two subsections, I discuss how the spatial and temporal characteristics of disruptions in brain memory networks from patients with AD pathology may be obtained and how the resulting connectivity models may be used to investigate optimum stimulation parameters for the alleviation of memory disruption. Finally, I compare the AD case to the PD case, where DCM has already been used to characterize the effects of DBS.

2 MODELING Modeling the effects of stimulation is critical in understanding the effects of DBS. Typically, simulation studies propose a set of differential equations that describe a network and examine DBS-induced signal propagation by exciting neuronally representative states such as membrane depolarization or firing frequency (Grant and Lowery, 2013; Mcintyre et al., 2004; Tass, 2003). DCMs lead to an optimized generative model of signal progression, and so the effects of stimulation can be readily examined in a given network. In particular, this chapter provides a blueprint using fMRI and EEG readout of multi- and single-site stimulation procedures. Overall, these in silico investigations might provide a functional map or blueprint for the optimization of both location and stimulation parameters. By performing these simulations, the idea is to mitigate trial-and-error approaches to the development of surgical neurostimulation interventions in neurodegenerative disease. A remaining question is how a reorganized circuit may lead to changes (improvements) in behavior or cognitive function. This is not explicitly explored in this chapter—rather we are attempting to map how to restore functional brain connections (using simulated DBS). However, the link to behavior is implicit since most DCM studies use particular cognitive or behavioral tasks to elicit task-relevant changes in brain connectivity. This chapter implicitly assumes that the first application of DCM to healthy and patient studies involves a task where optimal performance in controls is first linked to one or more particular connectivity measures. The assumption is that these connections strengths if reinstated would lead to better performance in patients with DBS. The overall advantage of this approach to testing DBS is that distributed networks are accounted for, thus potentially harmful, unintended downstream effects of DBS stimulation sites and frequencies can be identified prior to implantation.

2.1 PREDICTING STIMULATION EFFECTS USING DCM FOR FMRI A DCM is a generative model which links hidden neuronal population activity, expressed in terms of differential equations, to measured neuroimaging data through a biophysically motivated forward model (Friston et al., 2003b). The purpose of DCM is to compare competing mechanistic explanations, formulated in terms of

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synaptic connectivity and plasticity, for a given measurement. This is achieved by Bayesian model selection which rests on comparing the log evidence of competing models, a principled measure of model goodness which trades-off model accuracy and complexity (Mackay, 2003). Additionally, inverting (i.e., fitting) a chosen DCM provides the posterior distributions of model parameters, thus allowing for inference of synaptic mechanisms from the measured data. Since the initial description (Friston et al., 2003b), multiple DCMs have been developed. DCM for fMRI rests on a low-order Taylor approximation to the neuronal system of interest and only allows for relatively coarse estimates of synaptic mechanisms (Stephan et al., 2008). DCM represents a departure from alternative methods to estimate connectivity, because it employs a generative model of measured brain responses that takes into account the nonlinear and dynamic nature of neuroimaging signals. This is important for modeling potential unintended consequences of DBS and contrasts with functional connectivity measures that explore nondirectional statistical dependencies (often linear) between brain regions. A range of differential equation models exist for various imaging modalities and empirical data features. The current library of DCMs includes DCM for fMRI, stochastic DCM for fMRI, DCM for event-related responses, DCM for CSDs, DCM for phase coupling, DCM for induced responses, and DCM for neural fields (available within the academic freeware package, SPM12: http://www.fil. ion.ucl.ac.uk/spm). In DCM for fMRI, network sources or regions need to be preidentified—these are usually derived using standard mass univariate analyses. Then a generative model is used to test how these regions interact and how these interactions at the neuronal level would lead to changes in observable BOLD (blood oxygen leveldependent) responses. At the neuronal level, population dynamics evolve according to the form of a prescribed set of differential equations with inputs that correspond to designed experimental perturbations or endogenous brain noise (Eq. 1). Then at the observation level, contributing neuronal states form inputs to a second model: the Balloon model transforming neuronal activity to the required measurement space, i.e., BOLD responses (Stephan and Roebroeck, 2012). Together, these form a complete forward model that is inverted, given real data, using variational Bayes (Li et al., 2011). The neural population model describes the time evolution of unobservable neuronal states. In DCM for fMRI, these states, x, represent the rate of neuronal activity within a region whereby regions are modeled to exert an inhibitory influence on themselves (to prevent unstable dynamics) and either an excitatory or an inhibitory influence at other brain regions. These interactions are described by the parameter matrix of endogenous connectivity: A. A bilinear approximation is used to capture effects from designed experimental input modulations: B, which can alter either positively or negatively the strength of within- or between-region connections. D represents regional modulation of connections between other pairs of regions, for example, where prefrontal cortex will strengthen connections between regions in sensory areas of the model or graph. Finally the inputs, C represent the strength of timed perturbations delivered via to experimental stimuli v. These inputs and the states are also imbued with stochastic perturbations or endogenous noise, o.

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x_ ¼ A +

X k

u k Bð k Þ +

X

! xj Dð jÞ x + Cv + oðxÞ

j

(1)

ð vÞ

v ¼u + o

The parameters of the DCM {A, B, C, and D} as well as hyperparameters (controlling noise) would first be optimized for patients and controls. Then from the pathological state, the generative model could be augmented by further stimulation inputs to test whether the states x reorganize to more closely represent the healthy network output. This is discussed in detail below.

2.2 AUGMENTING PREDICTIONS USING DCM FOR EEG A wealth of cognitive EEG literature shows that the integration of regional neuronal activity, in the form of coordinated network processing, is required for complex cognitive processing and in memory tasks. These interactions are reflected in multiple EEG oscillatory bands, particularly at theta and gamma frequencies (Jensen et al., 1996, 2007; Moran et al., 2010; Nyhus and Curran, 2010). Using DCM for EEG, a model-based analysis is designed to uncover task-relevant brain network interactions. For example, broadband (2–60 Hz) oscillatory responses and dedicated spectral DCMs (Moran et al., 2007) could be used to inform the cell and receptor characteristics that support memory performance. Like DCM for fMRI, DCM for CSDs uses each individual’s imaging (electrophysiological) data features, specifically oscillatory characteristics to optimize (i.e., fit) an underlying biological model. These oscillatory features can be either broadband or have structure in certain regions of the frequency space and 1/f-type noise in others. For example in working memory studies, one may expect tasks to evoke prominent theta oscillations (4–8 Hz) and gamma oscillations (30–60 Hz) in prefrontal regions (e.g., Fig. 1), and many studies have implicated theta- and gamma-band oscillations in episodic memory tasks also (Fell and Axmacher, 2011; Mormann et al., 2005; Nyhus and Curran, 2010), where gamma oscillations in medial temporal and prefrontal cortex bind perceptual features, while theta oscillations provide long-range control from temporal to posterior brain regions and from PFC to hippocampus. These bands are also involved in prefrontal-dependent working memory tasks (Kami nski et al., 2011), with cross-frequency coupling an important feature. Other bands, such as alpha, have been shown to correlate with working memory task performance (Sauseng et al., 2005), while multiband effects (alpha and beta) have been observed in patients with AD compared to controls with subjective memory complaints (Pijnenburg et al., 2004). DCMs for CSDs (Moran et al., 2009) can accommodate interactions between brain sources across multiple frequencies. The idea would be to test putative mechanisms of synaptic connectivity that respond to mnemonic task demands and investigate the synaptic arrangements that best support encoding, maintenance, and recall during episodic and working memory tasks in healthy subjects, comparing those to connectivity measures obtained from patients with AD. The validity of the parameter inference in these models has been tested in

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FIGURE 1 DCM for MEG reveals synaptic substrates of improved memory performance. (A) Timing of a visuospatial working memory paradigm performed in young adults taking either placebo or levodopa (levodopa is a drug that increases dopamine availability through increased biosynthesis and potentiates dopamine receptor responses). Participants’ were more accurate at maintaining the image array on levodopa. (B) Effects at the sensor level during this maintenance period in different frequency bands: delta (2–4), theta (4–8), and alpha (8–16). Maintenance of the visual array was associated with enhanced delta and theta oscillatory responses in anterior electrodes, while alpha responses were sustained in posterior electrodes. (C) A source localization of sustained maintenance activity revealed the predominant source to be in superior frontal gyrus (SFG). Here, different theta effects were observed across the placebo/levodopa drug states. (D) A DCM was used to model the spectral response of SFG from 2 to 16 Hz. DCM parameters disclosed changes in synaptic signaling associated with increased memory performance. By estimating the contribution of ionotropic receptors (AMPA, NMDA, and GABAa) using a neural model of laminar-specific and connected cell assemblies, changes in their function were found. The validity of the in vivo human assay was reinforced by a striking quantitative effect on NMDA and AMPA receptor signaling that predicted behavioral improvement over subjects. Adapted from Moran et al. (2011a–c).

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rodents (Moran et al., 2008, 2011a) and humans (Moran et al., 2011c) and has demonstrated plausible synaptic estimates using, for example, known pharmacological effects (Fig. 1). DCM for CSD (Moran et al., 2009) utilizes neural mass models to describe activity within a region in a cortical network. In contrast to DCM for fMRI, additional neurobiological properties are represented in DCMs’ neural mass models owing to the enhanced temporal resolution. These include laminar-specific neuronal subtypes within a region as well as extrinsic connections between regions, and glutamate (AMPA and NMDA) and GABAergic (GABAa) receptor dynamics. The neural mass models used in DCM represent laminar structure by explicitly including in the source model either three or four subpopulations of cell types in granular, supragranular, and infragranular layers. These are interconnected through plausible intrinsic connectivity structures and include representations of pyramidal cells, inhibitory interneurons, and spiny stellate cells. The inversion procedure will then determine how parameters are weighted (including biophysical and forward parameters) to best fit the empirical scalp data. Inputs in the spectral domain are represented by a parameterized mixture of white and pink noise components. The time-domain equations are transformed to the frequency domain using the Fourier transform of the model’s first-order Volterra kernels, thus giving the transfer function mapping from the endogenous (neuronal) fluctuations to the scalp oscillatory data. For one region, a subpopulation may take the following form of equations to describe the dynamics: CV_ ¼ gL ðVL  V Þ+gAMPA ðVE  V Þ+gNMDA fMg ðV ÞðVE  V Þ+gGABAa ðVI  V Þ+u h  i  g_AMPA ¼ kAMPA A1 s V ext=int  gAMPA h  i  g_NMDA ¼ kNMDA A2 s V ext=int  gNMDA     g_GABAa ¼ kGABAa A3 s V ext=int  gGABAa (2) where the neuronal states now take on the form of postsynaptic membrane depolarizations (V) and channel conductances ( g). The depolarizations depend upon the particular reversal potential VE/I of the ion through that conducting channel, while the conductances depend on the time constants of the modeled receptor (1/kAMPA). The input to the population is described by the average afferent firing rate (s(V ext/int)), which is a sigmoidal transform of the afferent membrane depolarization. Like in DCM for fMRI, the parameters, A, describe the strength of connections between subpopulations within a region and the strength of connections between regions. Hypothesized connectivity architectures can be tested by altering the form of A as in DCM for fMRI. Similarly, B parameters can be added to A in a condition-specific way to represent contextual dependencies in network connections induced by experimental task manipulations. Previous neuroimaging studies addressing functional brain connectivity in AD have demonstrated profound disruptions in network interactions, particularly

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in the default mode network comprising medial prefrontal, posterior cingulate/ precuneus, and medial temporal regions (Buckner et al., 2005; Seeley et al., 2009; Supekar et al., 2008). The rationale for examining effective connectivity in AD patients here is to inform potential layer-specific targets for DBS, which could break hyperrhythmicity or hyporhythmicity in memory circuits to improve signal transmission and the capacity for plasticity (Rossor et al., 1984; Selkoe, 2002).

2.3 SIMULATING DBS EFFECTS USING DCM Predicting how the brain may respond to neurostimulation requires a system-level understanding of both the neurobiological substrates that may be affected and the downstream cognitive consequences. DCM in this context can be used to simulate the potential to restore aberrant network activity. For example, given the biophysical characterization of circuit changes underlying various memory tasks in patients with AD and age-matched controls, one could simulate the effects of focal stimulation at different frequencies and task-relevant locations (e.g., prefrontal cortical superficial layers) in order to measure whether the network can be reconfigured to the connectivity of the healthy state, both the local and the global-distributed dynamics. Using simple similarity metrics including correlations and dynamic time warping, one could then assess how single and multiple contact impulses could alter the characteristics of AD signals to more closely resemble the control cohort. In DCM for fMRI, the neuronal states, represented by x (Eq. 1), are controlled by connections between and within brain regions (A), and by modulatory changes (B) that occur in response to some input or task demand (u). Regions can also affect propagation along network pathways (this is the nonlinear component D). The regions can be directly excited by inputs—in which case they are given a separate state representation v, to accommodate fluctuations in incoming signals. These inputs excite dynamics with strength C. The parameters of a stochastic DCM are the matrices A, B, C, and D. These are fit to an individual’s dataset along with hyperparameters which control the noise (o) on each state. Having acquired estimates of A, B, C, D, and o, we can perturb the inputs with an external pacemaker u—to represent a stimulating electrode. The simulation test is then whether: ! X X ðkÞ ð jÞ x_C ¼ AC + u k BC + xj DC x + CC v + oðxÞ j

k ð vÞ

v ¼uexp + o x_P ¼ AP +

X

ðkÞ u k BP

+

X j

k ð vÞ

v ¼uexp + uDBS + o

! ð jÞ xj D P

x + CP v + oðxÞ

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where subscript C denotes the control network, subscript P denotes the patient network, and uDBS represents interventional DBS which could take any theoretical form, e.g., a sinusoid or a train of high-frequency impulses. The key goal is to restore the dynamics of xP to the dynamics xC, effectively compensating for broken connectivity in AP, BP, CP, and DP, with uDBS. We can then observe the resultant BOLD response throughout the entire network. An identical approach can be used in the EEG models where the parameterization and state space are more detailed. The equations for these DCMs follow conductance-based models and depict postsynaptic cell depolarization (V) that depends on specific conductances ( g), known physiological time constants (1/k), and the connections between layers and between regions A. Depicted in Fig. 2 are just those equations that would be used to describe the hypothalamus, where we can model two cell populations (typically cortical nodes will contain four subpopulations to model the laminar six-layer cortical architecture). Similarly, using the EEG-optimized models at this scale, we can assess the impact of an external input uDBS from a DBS electrode. The advantage of the EEG DCMs is that different layers and cell-type targets can be simulated.

FIGURE 2 Simulation studies of DBS. This illustration is designed to convey the key outcomes of the DCM-based imaging analysis and how they will be used to simulate interventions via DBS. At the bottom are the central equations in stochastic DCM for fMRI and in DCM for CSDs (see text).

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In total, the procedure would follow the following steps: 1. Perform a biophysical characterization of circuit connectivity in healthy control and patient brains using DCM for fMRI and DCM for CSDs. 2. Test for correlative structure among connectivity parameters and behavioral performance in patients and controls. Identify one or more candidate hyper- or hypoactive connections. 3. Using the patient-optimized DCMs simulate inputs u (Eqs. 1 and 2) using standard experimental design as well as HFS (u ¼ u + HFS) to mimic DBS. 4. Optimize placement of stimulating input and parameters of the stimulating waveform (amplitude, shape, and frequency) using the error between stimulation + patient and healthy control output data as a cost function. 5. Repeat using multicontact- or multiregion-based stimulation protocols.

3 APPLICATIONS 3.1 PREDICTING EFFECTS OF DBS IN AD There are now several accepted avenues for potential neuroprotection and for the maintenance of cognitive function in AD, including strategies that (i) reduce amyloid-beta burden, (ii) reduce neuroinflammation, and (iii) promote plasticity and synaptic communication (Longo and Massa, 2004). This third strategy—the promotion of plasticity and synaptic communication—could be supported by intervening using DBS. Given the difficulty in developing new pharmacotherapies that improve cognition in AD patients (Golde et al., 2011) and the current pharmacotherapeutic landscape where cholinesterase inhibitors provide only modest protective benefits (slowing cognitive decline), alternative or adjunctive strategies using neurostimulation are now being considered (Laxton and Lozano, 2013). Recent clinical trials of DBS for AD have targeted the fornix bundle and nucleus basalis of Meynert (Box 2) for the enhancement of memory and cognitive function. This electrode placement was motivated by a serendipitous finding in a patient who was undergoing DBS surgery for morbid obesity (Hamani et al., 2008). In this case, the patient—awake during the operation—experienced a vivid memory experience, which was confirmed to be an accurate long-term memory. A follow-up phase I clinical trial on six patients with AD (Laxton et al., 2010) revealed that DBS to the vertical portion of the fornix within the hypothalamus improved glucose utilization in temporal and parietal lobes. In relation to cognitive decline, slower than predicted rates of decline were observed in four patients and one patient at 6- and 12-month follow-ups, respectively—with preoperative disease severity negatively correlated with improvement metrics.

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BOX 2 ONGOING AND COMPLETED TRIALS FOR THE DEVELOPMENT OF DBS TO TREAT COGNITIVE AND MEMORY SYMPTOMS IN AD

Source: clinicaltrials.gov.

Though the trajectory of cognitive decline in AD varies across patients (Haxby et al., 1992), impairment of declarative memory encoding typically occurs first followed by disruption in implicit memory processes (Selkoe, 2002). The deterioration in executive function is also particular to certain domains—where dual task demands can impede function even when performance on those same tasks singularly reveals little or no impairment (Baddeley et al., 1991). Current DBS target regions—the hypothalamus (Hamani et al., 2008; Smith et al., 2012) and nucleus basalis of Meynert (Bakin and Weinberger, 1996)—are key modulators of cortical mnemonic networks mediating hormonal (e.g., corticotropin-releasing factor, vasopressin) and neuromodulatory (e.g., acetylcholine, serotonin) control over memory encoding (Miranda et al., 2003), consolidation (Roozendaal et al., 2008), and recall (Prast et al., 1996). However, other sites have yet to be tested, including “core” memory networks that include medial temporal and prefrontal regions. Moreover, the potential for using multiple stimulation sites exists given multicontact stimulation electrodes. Using DCM, one could assess memory modulations of descending, ascending, and corticocortical pathways to predict the most promising targets for stimulation intervention, determined by their relative contribution to memory dysfunction in AD. For example, one could examine activity at prefrontal cortex and its bidirectional connections with the medial temporal lobe, hypothalamus (Diorio et al., 1993), and the basal forebrain (Bigl et al., 1982; Fig. 2). DCM provides a model-based framework to test the likely configuration of information transmission between disparate brain regions during cognitive tasks. Using fMRI, one would assess whole-brain, cortical and subcortical activity and network effective connectivity in AD patients and age-matched controls performing a series of mnemonic functions, including long-term and working memory tasks. Having defined a multinode network for each task, a DCM using BOLD time

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series would be optimized to compare estimates of regional connectivity. These DCMs could also be supported by tractography analysis of MR diffusion images to embed structural as well as functional pathways. One could then establish the connection(s) with greatest behavioral predictability and assess whether common pathways emerge as maladaptive over multiple experimental paradigms. DCM for CSDs could be used to provide additional and complementary synaptic information to the connectivity measures obtained using DCM for fMRI. Of note, intrinsic regional cell types can be characterized in terms of their local connectivity—predicted using a multilaminar model of cortical macrocolumns (Moran et al., 2007). Having established the critical sources of activity using fMRI, EEG DCMs could then be constructed using fMRI as source priors to investigate oscillatory responses that characterize pathological signal propagation at cortical nodes in AD patients. In order to address the feasibility of HFS to redress pathological network transmission, an in silico investigation as outlined above could test whether subcortical–cortical, cortical–subcortical, or intracortical connections could be stimulated to restore pathological dynamic features to those present in healthy controls. Furthermore, these simulations could address whether HFS could accommodate synaptic plasticity by enhancing NMDA-mediated functional connectivity at inhibitory interneurons—a circuit disruption that emerges early in AD pathogenesis (Battaglia et al., 2007; Terranova et al., 2013). The EEG DCMs offer laminar- and cell-specific hypothesis-testing, while fMRI can be used to assess deep brain sources. Indeed, linking cortical EEG to subcortical network responses observed using fMRI has been recently demonstrated using DCM to assess the role of the brainstem in attention (Walz et al., 2013). Given that the optimal characterization (of connectivity) will be sought from healthy participants who can perform a cognitive task in which patients have a behavioral deficit, the hope is that altering the activity through the pathological network will also improve their behavior. Prior work has also employed DCM analysis to examine hypothalamic efferents and epileptogenic signal propagation in hamartoma-related seizure (Murta et al., 2012). There, directionality was resolved by measuring ascending and descending pathways, with concurrent consideration of corticocortical connections. This recent DCM analysis of seizure progression offers a nice analogy to the questions one could pose regarding abnormal AD pathways that link cortical and/or subcortical regions. Murta et al. (2012) tested long-standing and competing hypotheses regarding the pathways and epileptic signal propagation from hypothalamic regions to frontal regions in a single patient with hypothalamic hamartoma-induced seizures. Using DCM for fMRI, they found that the probable connectivity architecture involved a pathway from the hypothalamus to frontal cortex via a cingulate region, rather than a direct projection from hypothalamus to frontal cortex. Similar findings could be uncovered regarding hypothalamic, medial, and frontal connectivity in AD patients with memory impairment. The benefit of using DCM generally is that these types of architectures can be compared and statistically scored (where equal evidence would imply no features of the data can distinguish the directionality hypotheses).

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Moreover, by using stochastic DCM for fMRI (Li et al., 2011), more subtle endogenous fluctuations in these regions can be modeled. Obviously, EEG recordings will not contain enough depth information to localize activity to these sources. However in several previous assessments (e.g., of thalamic connectivity changes induced by anesthetics (Boly et al., 2012) and by a Parkinsonian model in rodents (Moran et al., 2011b)), DCM has been used with unobservable regions or “hidden nodes”—this means that the downstream effects of unobserved sources are used to account for dynamics in the model and the data. This “hidden node” analysis can be guided using the results from fMRI DCMs—where the relative importance of subcortical regions to memory failures and their connectivity with relation to cortical signal transmission can be measured.

3.2 TESTING THE ORIGIN OF EFFECTIVENESS OF DBS FOR PD Though DBS has been used in the past decades to successfully treat motor symptoms of PD, the mechanisms underlying its action are only now being elucidated. In humans, recent fMRI studies of patients with chronically implanted electrodes have used DCM to probe the circuit alterations induced by HFS to the STN (Kahan et al., 2012, 2014). This work by Kahan et al. has provided critical new data on circuit reorganization under the effects of STN DBS, and exemplifies how DCM can be used to interrogate complex multivariate system (cortical and subcortical) effects of DBS in neurodenegerative diseases. In their first study (Kahan et al., 2012), patients in the scanner performed a movement task (auditory cued joystick manipulation) off medication while both ON and OFF stimulation. Using a mass univariate analysis, brain regions associated with the interaction of DBS and voluntary movement were obtained—resulting in two active network nodes comprising insular cortex and thalamus. The authors used this two-region network to estimate the effective connectivity among these sources using a deterministic bilinear DCM, with the following neuronal dynamic form:          xthal xthal 0 b11 b12 a11 a12 ð3Þ ¼ + DBSON +C b21 b22 a21 a22 xi ns xi ns umove The parameters of the model included endogenous and reciprocal connections A that responded to the movement input (where the input strength was given by C). A model comparison was used to determine the form of the B matrix, representing the modulation of DBS-ON on the movement network connectivity. The authors observed that all B parameters were altered by DBS and in particular found that STN DBS enhanced the sensitivity of subcortical regions (represented by the thalamic BOLD response) to cortical (insular) input, while downregulating the subcortical effects on cortical areas during movement. These findings underscored the distributed effects of subcortical DBS, providing an important alternative account to traditional ideas where DBS may have been thought to imbue therapeutic effects via local regional effects alone.

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In a follow-up study (Kahan et al., 2014), stochastic DCM for fMRI (Eq. 1) was used to determine the effects of DBS on resting state brain connectivity. Here, the models were augmented with two states (Marreiros et al., 2008) to accommodate the GABAergic pathways from striatum (medium spiny cell projections) and from STN to GPi. Related to the discussion of hidden sources above, this study approximated STN dynamics where the precision of the noise was set close to zero, ensuring that data estimates were not based on fMRI from this region. This was necessary due to the stimulation artifact induced by DBS. The distributed response to DBS was affected by reduced STN afferents and efferents but with enhanced corticostriatal, thalamocortical, and direct basal ganglia pathways. Importantly, the study also observed that individual clinical status could be predicted by the DCM-based estimates of the hyperdirect, direct, and BG-STN connectivity strengths (Fig. 3). These studies were undertaken to characterize whole-brain effects of DBS and demonstrated the sensitivity of the DCM methodology with respect to individual pathology scores. Importantly, the multimodal approach of fMRI with electrophysiology is highlighted in the field of DBS for PD, given the supplementary and lower-level investigations using cellular-level biophysical models of subcircuits, e.g., GPi, thalamocortical relay properties, that have helped to uncover the relationship between DBS and pathological synchrony observed in PD (Cagnan et al., 2009).

4 DISCUSSION In AD, while great advances have been made in characterizing the pathogenic pathways of plaque and tangle formation, translating these discoveries into diseasemodifying treatments have been frustrated by the inability to prevent degradations in synaptic function and cell death, the neural substrates on which cognitive processes depend (Small and Duff, 2008). One alternative is to interrupt abnormal propagation along important neural pathways using neural stimulation (Bero et al., 2011; Jagust and Mormino, 2011). This general approach is supported by emerging hypotheses from molecular biology that posit that early insults from amyloid-beta could initiate a vicious cycle where resultant synaptic hyperexcitability exacerbates structural deficits and protein aggregates, causing disease progression and the march of cognitive decline (Palop et al., 2006). In PD, the primary pathology is the degeneration of midbrain dopaminergic neurons. DBS cannot reverse this neurodegenerative process; however, it has been found to have a substantial impact on patients’ quality of life. In fact, a recent review cautioned of the impact of such dramatic improvement in symptomology: “Quality of life improves substantially, inducing sudden global changes in patients’ lives, often requiring societal readaptation. STN-HFS is a powerful method that is currently unchallenged in the management of Parkinson’s disease, but its long-term effects must be thoroughly assessed. Further improvements, through basic research and methodological innovations, should make it applicable to earlier stages of the disease and increase its availability to patients in developing countries” (Benabid et al., 2009). This is a

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FIGURE 3 Two-state DCM representing major cortico-basal ganglia-thalamocortical looping circuits. The resting state analysis of ON-versus-OFF DBS revealed distributed changes in connectivity with ON inducing in particular an increase in the “direct” pathway (corticostriatal and striatothalamic inputs), while downregulating STN afferents and efferents. Adapted from Kahan et al. (2014).

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remarkable outlook for any treatment of degenerative disease and underscores the possibility of intervening in the functionality of brain circuits. In PD, beta-band synchrony appears to emerge as a downstream negative consequence of absent dopamine (with a nexus at the STN (Moran et al., 2011b)), and its desynchronization likely facilitates improvements in motor symptoms (Li et al., 2012) under DBS. To develop an analogous connectivity-centric view of memory dysfunction, i.e., to find the STN beta band and hyperdirect pathway of AD could lead to new computationally informed clinical trials of DBS for AD.

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