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Neuroscience Research

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Oscillotherapeutics – Time-targeted interventions in epilepsy and beyond
Yuichi Takeuchi a ,b ,⁎ , Antal Berényi a ,c ,d ,⁎a MTA-SZTE ‘Momentum’ Oscillatory Neuronal Networks Research Group, Department of Physiology, University of Szeged, Szeged, 6720, Hungaryb Department of Neuropharmacology, Graduate School of Pharmaceutical Sciences, Nagoya City University, Nagoya, 467-8603, Japanc HCEMM-SZTE Magnetotherapeutics Research Group, University of Szeged, Szeged, 6720, Hungaryd Neuroscience Institute, New York University, New York, NY 10016, USA

ABSTRACT

Keywords

Oscillatory brain activities support many physiological functions from motor control to cognition. Disruptionsof the normal oscillatory brain activities are commonly observed in neurological and psychiatric disorders including epilepsy, Parkinson’s disease, Alzheimer’s disease, schizophrenia, anxiety/trauma-related disorders, major depressive disorders, and drug addiction. Therefore, these disorders can be considered as common oscillation
defects despite having distinct behavioral manifestations and genetic causes. Recent technical advances of neuronal activity recording and analysis have allowed us to study the pathological oscillations of each disorder as
a possible biomarker of symptoms. Furthermore, recent advances in brain stimulation technologies enable timeand space-targeted interventions of the pathological oscillations of both neurological disorders and psychiatricdisorders as possible targets for regulating their symptoms.

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ARTICLE INFO
Oscillation
Closed-loop intervention
Transcranial electrical stimulation
Neuromodulation
Epilepsy
Psychiatric disorders
Oscillopathy
Oscillotherapeutics

1. Introduction: oscillations and neuronal activities are selforganized

In contexts of the neuroscience field, oscillations are rhythmic neuronal activities (Buzsáki, 2006). They are typically measured as fluctuating extracellular potentials by using electroencephalography (EEG),
electrocorticography (ECoG), intracranial local field potential (LFP) or
read out with functional brain imaging techniques or magnetoencephalography (MEG) each offering different time and spatial resolutions (Hong and Lieber, 2019). The major source of oscillations
is rhythmically synchronizing synaptic transmissions. The rhythmicity stems from network structures composed of distinct cell-types andthe population activities inside the network (Buzsáki et al., 2012).

For example, at mesoscopic network levels, inhibitory neurons are essential to generate oscillatory network activities; the interactions of excitatory pyramidal and inhibitory basket neurons via their reciprocal connections generate gamma band oscillations and sharp wave-ripples in
the hippocampus (HPC) (Buzsáki and Watson, 2012; Stark et al.,2014). At macroscopic network levels (the interaction between brain
regions), the medial septum (MS, a rhythmogenic basal forebrain nucleus) externally regulates theta band oscillations in the HPC (Kang etal., 2017). Emergent oscillations (the extracellular electrical field) thenorchestrates neuronal activities (the ephaptic effects) (Anastassiou etal., 2011). Thus, oscillations and neuronal activities in the brain are interdependent and self-organized. Oscillations reflect functional network
states, and they affect neuronal population activities in the network.

Abbreviations: 6-OHDA, 6-hydroxydopamine; AI, artificial intelligence; AD, Alzheimer’s disease; AMY, amygdala; BDNF, brain-derived neurotrophic factor; CSFA, cross-spectral
factor analysis; CSTC, corticostriatal-thalamocortical; DBS, deep brain stimulation; DLPFC, dorsolateral prefrontal cortex; DMN, default mode network; DSM-5, diagnostic and statisticalmanual of mental disorders; DSP, digital signal processor; DRT, dopamine replacement therapy; ECN, executive control network; ECoG, electrocorticography; ECT, electroconvulsive therapy; EEG, electroencephalography; FDA, food and drug administration; fMRI, functional magnetic resonance imaging; FPGA, field-programmable gate array; GAD, generalized anxiety
disorder; GPU, graphical processing unit; HD-tACS, high definition transcranial alternating current stimulation; HD-tDCS, high definition transcranial direct current stimulation; HPC,
hippocampus; ICA, independent component analysis; IoT, internet of things; ISP, intersectional-short pulse; LFP, local field potential; MAM, methylazoxymethanol acetate; MDD, major depressive disorder; MEG, magnetoencephalography; MS, medial septum; MPTP, 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine; NAc, nucleus accumbens; PCA, principal component analysis;
PCP, phencyclidine; PD, Parkinson’s disease; PFC, prefrontal cortex; PTSD, posttraumatic stress disorders; PTZ, pentylenetetrazole; PV, parvalbumine; REM, rapid eye movement; rTMS,
repetitive transcranial magnetic stimulation; SNc, substantia nigra pars compacta; STN, subthalamic nucleus; SUDEP, sudden unexpected death in epilepsy; tACS, transcranial alternating
current stimulation; tDCS, transcranial direct current stimulation; TES, transcranial electrical stimulation; tFUS, transcranial focused ultrasound stimulation; TI, temporal interference;
TMS, transcranial magnetic stimulation.
⁎
Corresponding authors at: MTA-SZTE ‘Momentum’ Oscillatory Neuronal Networks Research Group, Department of Physiology, University of Szeged, Szeged, 10 Dom sqr., 6720, Hungary.
E-mail addresses: yuichi-takeuchi@umin.net (Y. Takeuchi); drberenyi@gmail.com (A. Berényi)https://doi.org/10.1016/j.neures.2020.01.002
Received 27 November 2019; Received in revised form 18 December 2019; Accepted 19 December 2019
Available online xxx
0168-0102/© 2020.

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Many studies have revealed that oscillatory brain activities support
various brain functions such as motor control and cognition including
spatial memory (Girardeau et al., 2009), arbitrary representational
spaces (Agarwal et al., 2014; Solomon et al., 2019), sleep (Watson and Buzsáki, 2015), and emotions (Karalis et al., 2016; Likhtik
et al., 2014) via temporally coordinated interactions between multiple
brain regions (Bonnefond et al., 2017). Therefore, if oscillations are
disrupted (and the neuronal activity is consequently disrupted), normal
brain functions are supposed to be disrupted. If oscillations reflect bothnormal and pathological brain states, they could be good biomarkers ofsymptoms or behavioral phenotypes of neurological and psychiatric disorders. Oscillations are the dynamics of macroscopic neuronal circuits,
which is the closest to the behavioural phenotypes in the multiple levelsof biological structure (Fig. 1) (Leuchter et al., 2015). Thus, it is not
surprising that more-and-more studies show the coincidence of the temporal expression of pathological oscillations with that of the abnormal
behavioral phenotypes of neurological and psychological disorders (seeSection 2); these disorders are considered as ‘Oscillopathies’ (Mathalon
and Sohal, 2015). Pathological oscillations possibly come with botha correlation and a causal relationship with abnormal brain states andfunctions. If this is the case, the pathological oscillations may be a therapeutic intervention or modulation target for the disorders using the recently emerged time- and space-targeted brain stimulation technologies(Berényi et al., 2012; Vöröslakos et al., 2018). We call this strategy‘Oscillotherapeutics’.
In the following sections, we provide overviews on 1) the phenomenology of oscillopathies, 2) how we find abnormality in oscillationsin animal models and human subjects, 3) how pathological oscillatory
states emerge mechanically, a strategy for its intervention, 4) the embodiments of oscillotherapeutics in distinct stimulation modalities, and5) the engineering challenges of future clinical applications of oscillotherapeutics.

hal, 2015). We will briefly summarize abnormal oscillations of known
oscillopathies which can be targeted by interventions.
2.1. Epilepsy

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Epilepsy is a neurological disorder characterized by an enduring predisposition to generate epileptic seizures (Fisher et al., 2014). An
epileptic seizure is a transient behavioral change that might carry objective, overtsigns (e.g. convulsions) or subjective, covert symptoms (e.g.
loss of consciousness). These changes are most probably caused by abnormally synchronous neuronal activities in the brain. The synchronizedneuronal activity is quite evident in EEG measurements during seizures(ictal period) and the EEG synchronization is concomitant with behavioural manifestations such as tonic and clonic convulsions. Successful
pharmaceutical and surgical treatments of epilepsy consistently reduce
the frequency of electrographic and behavioural seizures (Glauser etal., 2006; Li and Cook, 2018). Furthermore, time-targeted intervention with the pathological neuronal oscillations of seizures or seizure
predictions suppresses the behavioural manifestation of seizures (Morrell, 2011). This strongly suggests a causal relationship between thepathological oscillation of EEG measurements and the symptoms of
epilepsy. Thus, epilepsy is a typical oscillopathy.

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2.2. Parkinson’s disease
Parkinson’s disease (PD) is a neurodegenerative disease characterized by tremor, rigidity, bradykinesia, and postural instability with theunderlying loss of nigrostriatal dopaminergic neurons (Soileau andChou, 2016). The pathological hallmark of PD is the finding of protein aggregates containing α-synuclein in neurons called Lewy bodies
throughout brain. Symptoms of PD are thought to be caused by the dysfunction of the corticostriatal-thalamocortical (CSTC) loop due to decreased dopaminergic tone.
Dopamine replacement therapy (DRT) is typically used to ameliorate the associated motor disturbances. There are pathological oscillations of tremor (4–7 Hz), double tremor (10 Hz), and beta (15–30 Hz)
frequencies in the CSTC loop of PD patients (Holt et al., 2019; Weinberger et al., 2009) and animal models (Deffains and Bergman,2019; Heimer et al., 2006). Disruption of pathological oscillations by
DRT (Heimer et al., 2006), the inactivation of the subthalamic nucleus(STN) (Wichmann et al., 1994), or deep brain stimulation (DBS) of
the STN (Deuschl et al., 2006) consistently decreases the motor disturbances of PD. This suggests a causal relationship between the pathological oscillations and symptoms of PD (Bergman et al., 2015). The target of the STN stimulation may be the corticostriatal axons (Gradinaru
et al., 2009). The intrinsic, slowly oscillating resting network activity,
measurable by fMRI, is called default mode network (DMN) (Raichle,2015). This network mostly consists of hub-like brain structures including the medial prefrontal cortex (PFC), the precuneus, and the posterior
cingulate cortex (Hagmann et al., 2008). In PD patients, there are PD
specific changes in the DMN (Delaveau et al., 2010; van Eimeren
et al., 2009). These changes were restored by DRT (Delaveau et al.,2010) and possibly will also be restored by DBS in the future (Kringelbach et al., 2011).

2. Oscillopathy – a phenomenological overview

Oscillopathy is defined as a neurological or psychiatric disorder inwhich abnormality in oscillatory brain activities is observed (Braunet al., 2018; Buzsáki and Watson, 2012; Mathalon and So

Fig. 1. The concept of oscillotherapeutics.A behavior is generated as a result of brain dynamics. Brain dynamics are determined by factors at various levels from genes to macroscopic network activity as a hierarchical system. The macroscopic circuit level is the closest to the behavior level. Therefore, oscillation (which reflects the dynamics of the macroscopic circuit level) supposes to have a close temporal correlation to (and an evident causal
relationship to) behavioral phenotypes. There are huge variations at both the gene andsubcellular levels of neurological and psychiatric disorders. In contrast, variations in thephenomenology of neurological and psychiatric disorders at the macroscopic circuit level(oscillation) are relatively minor. Therefore, time-targeted pathological oscillation intervention for neurological and psychiatric disorders (oscillotherapeutics) could be an effective strategy for regulating their behavioral phenotypes.

2.3. Alzheimer’s disease
Alzheimer’s disease (AD) is a chronic neurodegenerative diseasecharacterized by well-defined neurological features: neuronal loss, neurofibrillary tangle, and senile plaque. However, the clinical manifestations of AD as a major neurocognitive disorder are mainly psychiatricwhich include dementia, paranoia, depression and other cognitive defects: the Diagnostic and Statistical Manual of Mental Disorders (DSM-5)(American Psychiatric Association, 2013).

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Gamma oscillations typically result from the fast, reciprocal interactions of excitatory glutamatergic neurons and inhibitory GABAergic neurons in the brain (Buzsáki and Wang, 2012). These oscillations arethought to support many cognitive functions including working memory in the PFC (Benchenane et al., 2011; Roux and Uhlhaas, 2014).
Studies have found that gamma oscillations were disrupted in schizophrenia patients (Gonzalez-Burgos et al., 2015; Senkowski and Gallinat, 2015) and that this gamma disruption in the PFC presumably
leads to disrupted intra-PFC and PFC-HPC communications (Moran andHong, 2011). The decreased gamma oscillations are mediated by hypofunctional GABAergic networks in the PFC (Lewis et al., 2012). Thisfinding is supported by evidence that the gene expression of 67-kD isoform of glutamic acid decarboxylase, the key enzyme in GABA synthesis, is reduced in the post-mortem brains of schizophrenia patients (Akbarian et al., 1995; Volk et al., 2000). It has been reported that
repeated transcranial magnetic stimulation (rTMS) of the dorsolateralprefrontal cortex (DLPFC) restored gamma oscillations of schizophrenia
patients and cognitive functions concomitantly (Farzan et al., 2012).
This result suggests a possible causal relationship between the reduction in frontal gamma oscillation and cognitive deficit in schizophrenia(Pittman-Polletta et al., 2015).

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Oscillopathic endophenotypes of AD are explored mainly phenomenologically yet, and are summarized below (Cassani et al., 2018): (1)Slowing. The power spectrum shifts from high-frequency components(alpha, beta, and gamma) toward low-frequency components (delta andtheta) that are commonly seen in the resting-state EEG measurementsof AD patients (Jeong, 2004). This shift is proportional to the progression of AD and is thought to appear due to the decrease of cholinergic tones. (2) Reduced complexity. A decrease in the complexity ofthe brain electrical activity has been observed in AD patients (Jeong,2004). (3) Decrease in synchronization. Synchronization between brain
regions in AD patients decreases (Babiloni et al., 2016; Wen et al.,2015). The synchronization was evaluated using the Pearson correlation coefficient, magnitude coherence, phase coherence, Granger causality, phase synchrony, global field synchrony, and cross-frequency coupling. (4) Long-range, effective EEG connectivity (functional coupling)decreases (e.g. front-parietal, front-temporal) (Babiloni et al., 2016).
(5) The disruption of delta waves during slow wave sleep (Zott et al.,2018). These oscillopathic features were also reported with MEG and
fMRI studies (Engels et al., 2017; Greicius, 2008). Amyloid-beta andtau protein pathologies are at least partially causal to these oscillopathic
features of AD because amyloid-beta peptides and tau proteins affectexcitatory and inhibitory synaptic transmissions and thereby memory
functions in a concentration dependent manner (Gulisano et al., 2019;
Roberson and Mucke, 2006). Amyloid-beta peptides are known to disrupt the excitatory/inhibitory balance by interfering with GABAergic interneurons as well (Mably and Colgin, 2018).
Furthermore, acute application of soluble amyloid-beta alone canacutely and reversibly disrupt synchronizing slow waves across the cortex, thalamus and HPC during non-rapid eye movement (REM) sleep-likeanaesthetized mice (Zott et al., 2018). The reduction in gamma oscillations in AD animal models is commonly observed (Mably and Colgin,2018). Inversely, the artificial induction of gamma oscillations in thebrain decreases amyloid-beta depositions, prevents neuronal loss, andimproves cognitive functions in AD animal models (Adaikkan et al.,2019; Iaccarino et al., 2016; Martorell et al., 2019). The introduction of high-frequency oscillatory activity into the brain via fornix DBS
decreases amyloid-beta deposition in a rat AD model (Leplus et al.,2019) and improves cognitive functions in both AD animal models andpatients (Mirzadeh et al., 2016).

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2.5. Anxiety and trauma-related disorders

Generalized anxiety disorder (GAD) and post-traumatic stress disorders (PTSD) are characterized by chronic and exaggerated anxiety andfear (American Psychiatric Association, 2013). Conceptually, these
disorders can be related to (1) the overgeneralization of perception, interpretation, and assessment of innocuous stimuli and (2) overexpression of anxiety and fear responses. The former is explained by the disruption of the PFC, pattern separation of the HPC, and intrinsic sensory
hyperactivity in the primary sensory cortex. For example, resting EEG
recordings from PTSD patients revealed that there was disrupted sensory processing with intrinsic sensory hyperactivity in the visual cortex (suppressed alpha power), decreased bottom-up alpha power-mediated inhibition to the frontal cortex, and increased frontal gamma band
(30–50 Hz) power activated by the intrinsic sensation (Clancy et al.,2017). The latter can be explained by enhanced neural activity in thenegative emotion networks. For example, Huang et al. (2014) foundhyperactivities in the AMY, the HPC, and the insular cortex in resting
MEG recordings of PTSD patients, which are supposed to positively correlate with their symptoms.
In addition, Qiao et al. (2017) found stronger functional connectivity with the AMY, insular cortex, putamen, thalamus, and posterior cingulate cortex (which are negative emotion circuits) in resting state fMRI
recordings of GAD patients. In contrast, they found that weaker connections in the frontal and temporal cortices in GAD patients. Interestingly, they also found decreased effective connectivity (Granger causality) from the frontal cortexes to the AMY and basal ganglia (Qiao et al.,2017), which is the top-down inhibitory activity control of the subcortical networks. The MEG study also reported that the alpha (8–12 Hz)
activity of PTSD patients decreased in the DLPFC and ventromedial PFC,
and it also decreased the top-down alpha causality from these structures(Huang et al., 2014). Other resting state EEG studies support hypofunction of the frontal cortexes in GAD and PTSD patients (Crost et al.,2008; Eidelman-Rothman et al., 2016; Veltmeyer et al., 2006).
Rodent studies have shown the role of the PFC in regulating the limbic
system as a top-down control of fear expression and anxiety, which are
mediated by theta or alpha range functional couplings (Dejean et al.,2016; Karalis et al., 2016; Likhtik et al., 2014). Therefore, on-demand modulation of the top-down control may be effective for supressing excessive anxiety and fear expression.

2.4. Schizophrenia

Schizophrenia is a severe psychiatric illness characterized by positive symptoms including delusions, hallucinations, or paranoia, and negative symptoms including a loss of motivation, apathy, asocial behavior,loss of affect, and poor use and understanding of speech. Schizophrenia patients also have cognitive symptoms such as impaired working
memory, dissociated thought processes, and impaired executive function (Sontheimer, 2015). Because of an absence of unequivocal biomarkers, schizophrenia is diagnosed entirely on the assessment of symptoms by a trained psychiatric doctor who bases his or her judgment on
a number of features described in DSM-5 (American Psychiatric Association, 2013). Therefore, efforts have been made to find an appropriate biomarker using functional brain imaging techniques, EEG and
MEG recordings etc. (Meyer-Lindenberg, 2010). For example, fMRI
studies revealed that PFC activity was reduced in schizophrenia patients(Barch et al., 2001); the PFC governs executive function, task initiation, motivational drive, and working memory. Reduced activities havebeen reported on the amygdala (AMY) and the HPC (Meyer-Lindenberg, 2010), which could explain the flat affect of individuals of schizophrenia. Resting state networks have also been changed in schizophrenia patients (Alexander-Bloch et al., 2012; Cabral et al., 2012).

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2.6. Major depressive disorder

In addicted brains, many neuroadaptations happen from epigenetic
to neurocircuit levels. These neuroadaptations contribute to chronic,
obsessive drug intake behaviors and impulsive decision-making in the
preoccupation/anticipation stage. For example, the neural activity of
the PFC is reduced (hypofrontality) in addicted patients (e.g. smokers)(Goldstein and Volkow, 2011; Zilverstand et al., 2018). This reduced activity would presumably be a cause of impulsive decision-making of addicted patients (Bechara, 2005) because the PFC governs
analysis, prediction, and the executive control of reward seeking behaviors (Kennerley and Walton, 2011).
Abnormal frontal EEG measurements during the resting state arealso observed in opioid users (Motlagh et al., 2017), alcohol abusers(Huang et al., 2018), tobacco smokers (Li et al., 2017), cannabis
users (Prashad et al., 2018), and psychostimulant users (Newton etal., 2003), although their abnormalities are dependent on drug modalities (Newson and Thiagarajan, 2019). The oscillation abnormality
in the frontal cortex is context dependent as well. For example, frontal
asymmetry (left lateralization effects, less alpha oscillation in the left
hemisphere) occurred in cocaine abusers in response to losing on theirchoice of immediate large rewards during the Iowa gambling task (Balconi et al., 2014). These disrupted frontal oscillatory activities are also
represented by the reduction of the top-down inhibitory control called
the executive control network (ECN), which for example controls the desire salience for drugs (Bechara, 2005). Together with the DMN andthe salience network, the decreased ECN activity is a good predictor of
the cravings of chronic tobacco smokers (Lerman et al., 2014; Sutherland et al., 2012). The restoration of the ECN by rTMS of the left
DLPFC alleviated nicotine craving with significant EEG power changes(Pripfl et al., 2014).
Therefore, an obsessive drug taking habit driven by drug craving andimpulsive decision-making during the preoccupation/anticipation stage
may be treated with non-invasive or invasive stimulation (Dandekar etal., 2018; Diana et al., 2017), or cognitive interventions (Zilverstandet al., 2016) by modulating the oscillating networks where the PFC iscentral. Negative reinforcement during the withdrawal/negative affectstage (which can be mediated by delta and gamma band activities in
the limbic system including the PFC and the nucleus accumbens (NAc)(Dejean et al., 2013, 2017)) may be intervened by DBS or non-invasive stimulation (Dandekar et al., 2018; Diana et al., 2017). Positive
valence during the binge/intoxication stage (which is presumably mediated by delta-band activity in the NAc (Wu et al., 2018)) may be replaced by DBS of the NAc or the medial forebrain bundle (Dandekar etal., 2018). These facts indicate the oscillopathic features of drug addiction and the possible applications of oscillotherapeutics.

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Major depressive disorder (MDD) is a common and persistent psychiatric disorder characterized by extreme feelings of sadness and low
mood disproportionate to any possible cause (American Psychiatric
Association, 2013). MDD results in tremendous societal costs (Greenberg et al., 2003). Intracranial electrophysiological recordings fromepilepsy patients indicate that mood can be decoded from multi-channel
LFP recordings in the limbic system, including the orbitofrontal cortex,
the cingulate cortex, the AMY, the HPC, the superior frontal cortex, andthe middle frontal cortex (Reardon, 2017; Sani et al., 2018). Vulnerability to stress and susceptibility to depression have been decoded frommulti-channel recordings in the limbic system in animal models of depression (Hultman et al., 2016, 2018). These models indicate the oscillopathic nature of depression.
The oscillopathic features of MDD are summarized as follows(Baskaran et al., 2012; Eidelman-Rothman et al., 2016; Fitzgerald and Watson, 2018): (1) elevated alpha band activity in the temporoparietal region; (2) elevated frontal theta band activity; (3) alphafrontal asymmetry (left hemispheric hypoactivity and right hemispheric
hyperactivity represented as alpha, theta, and beta band activities); and(4) decreased gamma band activity. These features relate to MDD symptoms and predict the effectiveness of pharmacological treatment using tricyclic antidepressants and selective serotonin reuptake inhibitorsand electroconvulsive therapy (ECT). This suggests their usefulness asa biomarker of depression disorder. In addition, fMRI studies suggestthat the DMN, the cognitive control network, and the affective network
were functionally hyperconnected in depression patients (Sheline etal., 2010). Functional decoupling of these networks by neuromodulation techniques may relieve depression symptoms (Fox et al., 2012;
Liston et al., 2014).
Furthermore, there may be causal relationships between oscillation
disturbances and depression symptoms. First, restoration of the frontal
alpha symmetry using anodal transcranial direct current stimulation(tDCS) on the DLPFC (Loo et al., 2012) and specifically neurofeedback (Mennella et al., 2017) improved depression symptoms. In addition, subanaesthetic dose of ketamine (0.5 mg/kg) reduced delta oscillations (1–5 Hz) and increased gamma oscillations (45–85 Hz) in the human cortex and improved depressive mode of patients (Berman et al.,
2000; Hong et al., 2010). Furthermore, high frequency rTMS on theleft DLPFC increased resting state gamma oscillations in the frontal cortex and improved the depressed mood in patients (Noda et al., 2017).
These pathological oscillations can be targeted using molecular (pharmacological), network (neuromodulation), and cognitive (behavioural)
methods to interrogate depression symptoms (Leuchter et al., 2015).

3. Mapping of oscillopathies

2.7. Drug addiction

Appropriate animal models for each disease or disorder are required
to facilitate development of oscillotherapeutics. Appropriate recording
techniques for the oscillating neuronal activities of animal models andhumans are indispensable, as are efficient analytical methods. Thus,
here we provide an overview of how the pathological oscillations of neurological and psychiatric disorders are recorded and detected (the ‘diagnostic’ in research and clinics).

Drug addiction (also known as substance use disorder) is a chronically relapsing disorder characterized by persistent drug seeking anddrug-taking behaviors despite significant negative physical, emotional,social and occupational consequences (Volkow and Morales, 2015).
Drug addiction progresses from an impulsive to a compulsive intake in acollapsed cycle that consists of three stages: (1) preoccupation/anticipation, (2) binge/intoxication, and (3) withdrawal/negative affect (Kooband Volkow, 2010). At the beginning, the voluntary or impulsive intake induces euphoria during the binge/intoxication stage and positive
reinforcement will drive the next intake. After establishment of maladaptation (addicted state), negative reinforcement caused by relief ofanxiety, stress, and/or restlessness during abstinence will be a drive forfurther intake (Volkow et al., 2016). In the preoccupation/anticipation stage, patients have a craving, and obsession to get drugs.

3.1. Animal models

3.1.1. Animal models for epilepsy
Animal models for epilepsy research are thoroughly summarized
in the book of Pitkä n en et al. (2017). Briefly, the epilepsy models are classified by seizure types (generalized or focal, petit mal or
grand mal), animal species (mice, rats, cats etc.), whether in vitro or invivo, whether genetic or acquired, whether acute or chronic, and how

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OHDA rodent model and the MPTP-treated monkeys exhibit pathological oscillations in their basal ganglia as frequently observed in humanpatients: tremor (4–7 Hz), double tremor (10 Hz), and beta (15–30 Hz)(Deffains and Bergman, 2019; Heimer et al., 2006). Mutationsof causal genes or genetic risk factors of Parkinson’s disease are modelled in mice and rats including SNCA (alpha-synuclein), PRKN (parkin),
PINK1 (PTEN-induced putative kinase 1), DJ-1 (PARK7) and LRRK2(leucine-rich repeat kinase 2). These models offer ways to study pathology as Lewy-body like inclusions, but they exhibit only mild motor
symptoms. Pathological oscillations in the basal ganglia in these genetic
models have not been studied well yet.

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each seizure is evoked (electrical, chemical, sensory inputs, spontaneousetc.).
Chronic spontaneous seizure models are typically employed for thedevelopment of time-targeted closed-loop interventions. Tottering (tg)and Stargazer (stg) mouse strains are available to study absence (petit mal) seizures with spike-and-wave discharges. These strains have
known mutation on alpha and gamma subunits of voltage-dependentcalcium channels, respectively (Upton and Stratton, 2003). Two inbred strains are for example available for rat experiments: the genetic absence epilepsy rats from Strasbourg (GAERS) and the Wistar
Albino Glaxo strain (WAG/Rij) (Coenen and van Luijtelaar, 2003;
Danober et al., 1998). The spike-and-wave discharges are seen in ordinal outbred laboratory rats and even in wild-caught rats as well (Taylor et al., 2019). Absence seizures can induced acutely by systemic
administration of a single pharmacological compound [4,5,6,7 tetrahydroxyisoxazolo (4,5,c) pyridine 3-ol (THIP), low dose pentylenetetrazole (PTZ), or gamma-hydroxybutyrate] in rats (Fariello and Golden,
1987; Marescaux et al., 1984; Snead, 1988) and chronically by prepuberty systemic administration of AY-9944 or methylazoxymethanol
acetate (MAM)-AY in rats (Cortez et al., 2002; Serbanescu et al.,2004).
The systemic injection of GABAA receptor antagonists (e.g. PTZ, bicuculline, picrotoxin) can induce acute generalized convulsion seizures inrodents (Mackenzie et al., 2002; Velisek et al., 1992; Velíšková
et al., 1991). The systemic injection of glutamate receptor agonists(e.g. kainic acid and NMDA) or muscarinic receptor agonists (e.g. pilocarpine) can also induce acute, generalized, convulsive seizures in rodents (Ben-Ari et al., 1981; Mareš and Velišek, 1992; Turski et al.,1983). In addition, the inhalation of flurothyl can be used (Prichard
et al., 1969), along with intracranial injections of bicuculline, picrotoxin, kainic acid and other drugs to induce acute convulsive seizures inrodents (Ben-Ari et al., 1980; Sierra-Paredes and Sierra-Marcuño,
1996; Velíšková et al., 1991). A chronic spontaneous, limbic seizure
rodent model can be prepared by the repeated systemic injection of PTZ,
kainic acid, or pilocarpine (Cain, 1981; Cavalheiro et al., 1991; Hellier et al., 1998), a single intrahippocampal injection of kainic acid
(Bragin et al., 1999), or repeated daily electrical stimulation of thelimbic structure (e.g. the AMY, HPC) (Goddard et al., 1969; McIntyre and Gilby, 2009). Genetic models for spontaneous convulsive
seizures are available both in mouse and rat strains (e.g. weaver mice,
NER/Kyo rats) (Serikawa et al., 2015; Upton and Stratton, 2003).
Auditory stimulation can induce-convulsive seizures in the genericallyepilepsy-prone rats (GEPRs) and DBA/2 mice (De Sarro et al., 2017).

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3.1.3. Animal models for Alzheimer’s disease
Most AD patients are sporadic and there are some animal models forsporadic AD using metabolic and traumatic brain injury-induce damage
etc. (Zhang et al., 2019). However, the vast majority of current AD
animal models are transgenic rodents (mainly mice) and are based on
the amyloid and tau hypotheses, and the genetics of the familial formof the disease (Mullane and Williams, 2019). Nearly 170 transgenic/
knock-in/knock-out models of AD have been developed to date (ALZ FORUM Research Models Database; https://www.alzforum.org/researchmodels). They are principally focused on mutations in APP (Amyloid
precursor protein), PSEN1 (presenilin 1), MAPT (microtubule-associatedprotein tau), and Trem2 (Triggering receptor expressed on myeloid cells2), and APOE (apolipoprotein E), as well as the transfection of the amyloid processing enzyme, BACE1 (Beta-Secretase 1) (Götz et al., 2018;
Mullane and Williams, 2019). The model animals have single ormultiple mutations of these genes. For example, 3×Tg mice, whichhave APP KM670671NL (Swedish), MARPT P301 L, and PSEN1 M146 V
triple mutations, show amyloid beta plaque, hyperphosphorylated tau,and neurofibrillary tangle as pathological phenotypes and deficits inworking, spatial, and fear conditioning memory (Oddo et al., 2003).
5 × FAD mice have three mutations on APP (Swedish, Florida, London)
and two mutations on PSEN1, and they show amyloid-beta plaque andmemory deficits as soon as two months old (Oakley et al., 2006). In
the tau pathology model, rTg4510 mice with MAPT P301 L mutation
have neurofibrillary tangle, neuronal loss and memory deficits as phenotypes (Santacruz et al., 2005). The overexpression of mutant human
APOE4 protein (a risk factor of AD) in APOE4-KI mice results in significant memory impairment as well (Sullivan et al., 2004).
Importantly, all these AD models (3×Tg, 5 × FAD, rTg4510,
APOE4-KI) consistently show a reduction of slow gamma oscillation in
the CA1 of HPC (Booth et al., 2016; Gillespie et al., 2016; Iaccarino
et al., 2016; Mably et al., 2017), which contributes to the encoding
and retrieval of memory. CA1 place cell representations of space were
unstable in these mice and the deficits in slow oscillations in the HPC
were concomitant with spatial memory. Surprisingly, the optogenetic
activation of parvalbumine (PV)-interneurons at slow gamma frequencies (or 40 Hz light flicker sensory stimulation) reduced amyloid-beta
depositions in the brain and restored cognitive impairment of the AD
mice model (Iaccarino et al., 2016; Martorell et al., 2019).
In the APP23 × PS45 mouse model (Busche et al., 2008), the coherence of slow waves between different cortical regions, the thalamus,
and the HPC is completely disrupted in the light anesthesia condition(Zott et al., 2018). This resembles disrupted, slow-wave oscillations
during natural non-REM sleep in AD patients (Winer et al., 2019). The
coherent slow wave oscillations were transiently disrupted in wild-typemice by the application of soluble amyloid-beta, which suggests a causal
relationship between amyloid-beta and the pathological oscillation pattern in AD (Busche et al., 2015).

3.1.2. Animal models for Parkinson’s disease
Animal models of PD are classified into neurotoxin models and genetic models (Gubellini and Kachidian, 2015). 6-OHDA (6-hydroxydopamine) and MPTP (1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine)
are typically used to mimic selective loss of nigrostriatal dopaminergic
neurons via mechanism by which mitochondrial complex I is blocked(Tieu, 2011). Both neurotoxins are used in rodent and non-human primate experiments.
Systemically administered MPTP can easily cross the blood brain barrier whereas 6-OHDA should be stereotaxically injected into the target
brain structure (usually the substantia nigra pars compacta (SNc), themedial forebrain bundle, or the striatum). 6-OHDA and MPTP administrations lead to significant PD-like motor symptoms including akinesia, freezing, bradykinesia, muscle rigidity, abnormal posture, stereotypy and tremor associated with significant degenerative loss of SNc
dopaminergic neurons (Smeyne and Jackson-Lewis, 2005). Neither
6-OHDA nor MPTP administration induces Lewy body-like inclusions
with alpha-synuclein (Cenci et al., 2002). Importantly, both the 6-5

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terneurons, which impairs HPC synchrony, spatial representations, andworking memory in mice (Korotkova et al., 2010). The NMDA hypofunction also reduces delta and theta activity in the cortex and HPC(Kiss et al., 2013). Ketamine also disrupts the theta modulation of
gamma-band activity and reduces network responsibility to the environment in a computer model of HPC (Neymotin et al., 2011).
As a gene-environment interaction model, WISKET rats were reported as a selectively bred line with schizophrenia-like phenotypes(reduced sensorimotor gating, hyperalgesia, and memory deficit) after
post-weaning social isolation and chronic ketamine treatment over 15
generations (Büki et al., 2018). The WISKET rats showed increased
theta, alpha, and beta-band activities and reduced gamma-band activities in ECoG recordings (Horvath et al., 2016).

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3.1.4. Animal models for schizophrenia
Animal models of schizophrenia mostly fit into one of four different induction categories: developmental, drug-induced, lesion or genetic
manipulation models (Jones et al., 2011). Examples of neurodevelopmental models include gestational MAM injections, bacterial or viral infections, and post-weaning social isolation; pharmacological models include amphetamine-induced psychosis, NMDA antagonist [phencyclidine (PCP), MK-801, ketamine)]-induced psychosis; lesion models
include neonatal ventral HPC lesion; genetic models include various
knock-out or mutant models of schizophrenia susceptibility genes, someof which were validated by genome-wide association studies (Schizophrenia Working Group of the Psychiatric Genomics Consortium,2014).
These models resemble various cognitive symptoms found in schizophrenia patients including deficits of sensorimotor gating, working
memory, visio-spatial memory, and object recognition, as well as decreased social interaction, increased locomotion, and exaggerated sensation etc. The models also show cellular or circuit level alterations including decreased synaptic connections, and spine densities, the lossof prefrontal PV-positive interneurons, the loss of dendrites in corticalpyramidal neurons, and pathological oscillations (e.g. dysfunctional prefrontal gamma oscillations). The pathological oscillations tie the cellular or circuit level pathophysiology to alterations in local processing andlarge-scale coordination, and in turn may lead to cognitive and perceptual disturbances observed in schizophrenia (Pittman-Polletta et al.,2015; Senkowski and Gallinat, 2015).
A number of schizophrenia-susceptibility genes have been identified
on chromosome 22. These include DISC1 (disrupted-in-schizophrenia 1),
NRG1 (neuregulin 1) and its receptor ERBB4 (erb-b2 receptor tyrosine
kinase 4, erbB-4), and COMT (catechol-O-methyltransferase). DISC1 is
a synaptic protein that plays a crucial role in synaptogenesis (Bennett
AO, 2008). Mutations or the functional disturbance of DISC1 lead to thedisruption of PV-positive interneuron cytoarchitecture and hypofunctionin the cortex and HPC, which is critical for normal oscillatory activity
in the brain (Koyama et al., 2013; Nakai et al., 2014). NRG1 and
ERBB4 are also synaptogenic schizophrenia susceptible genes (Mei and
Xiong, 2008). Their disruption resulted in abnormal gamma oscillations in the HPC and disrupted functional coupling between the ventral
HPC and NAc (Koyama et al., 2013; Nason et al., 2011). Reduced
dysbindin-1 (another synaptic protein from susceptible gene DTNBP1(Dickman and Davis, 2009)) is associated with reduced phasic activation of PV-positive interneurons and reduced gamma oscillations (Carlson et al., 2011).
One of the largest risk factors for schizophrenia is the microdeletionof chromosome 22q11.2 that wipes out up to 60 genes; the 22q11.2
deletion syndrome results in facial abnormalities, heart defects, and anumber of neuropsychiatric conditions (Jonas et al., 2014). A quarterof the patients that have the microdeletion of this chromosome develop
schizophrenia. Importantly, in the Df(16)A+/− mouse model of this micro delision, mice exhibited reduced PFC-HPC synchrony, represented
by reduced phase-locking of PFC neurons to HPC theta oscillation anddisrupted coherence across multiple frequency ranges (delta to gamma
ranges) (Sigurdsson et al., 2010).
As pharmacological models, NMDA receptor blockers such as ketamine and PCP are known to induce delusions and hallucinations inotherwise healthy subjects (Krystal et al., 1994). Ketamine is known
to attenuate both background and sensory evoked theta power in the
CA3 in mice. It enhances both background and evoked gamma power,
but it decreases relative-induced gamma power (Lazarewicz et al.,2010). This suggests that ketamine decreases the signal-to-noise ratio of
gamma-band activity and possibly leads to disrupted pattern separation
function in the CA3 region, contributing its dissociative feeling. Ketamine reduces NMDA receptor function preferentially on PV-positive in

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3.1.5. Animal models for anxiety and trauma-related disorders
Animal models for anxiety and trauma-related disorders are classified into five models: experience-based, pharmacologic, pharmacological lesion, selectively bred genetic, and specific transgenic (Hoffman,2016). Examples of experience-based models include fear conditioning and extinction, pre-weaning stress, and maternal deprivation. Pharmacologic models include yohimbine (alpha-2 adrenergic receptor antagonist), CCK tetrapeptide (CCK-4, an anxiogenic neuropeptide), caffeine (adenosine receptor antagonist), m-chlorophenylpiperazine (serotonin 5-HT2C receptor antagonist), and FG7142 (benzodiazepine partial
inverse antagonist). Pharmacological lesion models include the chronic
infusion of l-allylglycine (an inhibitor of glutamic acid decarboxylase)
into the dorsomedial/perifornical region of the hypothalamus (DMH/
PeF) in rats (Johnson and Shekhar, 2012). Selectively bred genetic
models include Roman High and Low Avoidance rats (Escorihuela etal., 1999), Sardinian alcohol-preferring rats (Colombo et al., 1995),
High anxiety behavior and Low anxiety behavior rats (Yilmazer-Hanke
et al., 2004), Floripa H and L rats (Ramos et al., 2003), Ultrasonic rats (Brunelli and Hofer, 2007), and High anxiety behavior
mice (Erhardt et al., 2011). Specific transgenic models include 5-HTtransporter knockout mice, brain-derived neurotrophic factor (BDNF)
Val66Met mice, COMT and monoamine oxidase A deficient mice,
5-HT1A knockout mice, corticotropin-releasing hormone overexpression
mice, and neuropeptide Y-knockout mice.
Their endophenotypes can be measured as startle reactivity, behavioral inhibition (via the open field and elevated plus mazes, as wellas the light/dark, social interaction, and punished conflict tests), carbon dioxide sensitivity (avoidance of a CO2-enriched environment, exploratory behavior after exposure to CO2-enriched air, tidal respiratory
volume during exposure to CO2-enriched air), and fear over-generalization (discrimination of CS+ and CS− stimuli after fear conditioning).
Recent studies have revealed that distinct oscillatory activities in specific PFC-AMY-HPC networks are related to both fear/anxiety expression
and its regulations (Çalışkan and Stork, 2019; Dejean et al., 2016;
Karalis et al., 2016; Likhtik et al., 2014).
3.1.6. Animal models for depressive disorders
Animal models for depressive disorders are classified into five models: experience-based, pharmacologic, lesion, genetic, and gene-environment interaction (Hoffman, 2016). Experience-based models include
learned helplessness, chronic adult stress (e.g. overnight illumination,water or food restriction, tilting cages, social isolation or crowdingetc.), early life stress (e.g. maternal separation), and social stress (e.g.
chronic social defeat). Pharmacological models include withdrawal frompsychostimulant use. Lesion models include bilateral olfactory bulbectomy. Genetic models include selectively bred lines (e.g. the Rousen
depressed mouse line, Flinders Sensitive Line rats, Wister Kyoto rats,
Fawn Hooded rats, SwLo/SwHi rats, cLH rat lines) (El Yacoubi etal., 2003; Henn and Vollmayr, 2005; Overstreet and Wegener,
2013; Rezvani et al., 2007; Will et al., 2003), and specific trans6

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sional spatiotemporal structure of oscillations in a brain region (Agarwal et al., 2014; Berényi et al., 2014). For example, Oliva et al.
found that sharp-wave ripple in the CA2 subregion precedes those in the
CA1 and CA3 subregions in the rat HPC (Oliva et al., 2016).
The insertion of multiple silicon probes and/or wire electrodes into
distinct brain regions allowed oscillatory interactions between brain regions to be explored during spatial navigation (Fernández-Ruiz et al.,2017), goal-directed behaviors (Fujisawa and Buzsáki, 2011), epileptic seizures (Berényi et al., 2012), anxiogenic conditions (Girardeau
et al., 2017), depression (Hultman et al., 2018), and drug addiction (Sjulson et al., 2018). Matrix silicon probes can be used to obtainhigh-density three-dimensional oscillation activities in the brain (Rioset al., 2016), and flexible mesh electronics used instead of rigid electrodes enable year-long stable recordings (Hong et al., 2018). Two-dimensional electrodes on flexible polymer sheets enable potential recordings from the cortical surface (Khodagholy et al., 2015). Simultaneousrecording of brain and other physiological oscillations (e.g. electrocardiogram, electromyogram, and breathing) from freely-moving animals
is an important technique to study pathophysiological representations ofneuropsychiatric disorders (Sasaki et al., 2017).

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genic lines (e.g. 5-HT transporter knock-out rats and mice, BDNF promoter IV-mutant mice, BDNF Met mice) (Chen et al., 2006; Sakata
et al., 2010; Wisor et al., 2003). Their cognitive/behavioral phenotypes such as anhedonia can be measured using the sucrose preference
test, conditional place preference, intracranial self-stimulation, variable
progressive ratio reinforcement, and response bias probabilistic reword
task; they can be measures as negative processing bias using increased
reactivity to aversive stimuli, probabilistic reversal learning, and reactivity to emotionally ambiguous cues. Some physiological endophenotypes (e.g. sleep pattern changes) are recapitulated as well in these rodent models.
Recently, stress vulnerability and depression susceptibility were successfully decoded from large-scale electrophysiological recordings as
distinct oscillation patterns in freely moving mice (Hultman et al.,2018). The specific oscillation patterns for the vulnerability and susceptibility are consistent with the results of pharmacological (interferon administration) and early life stress (maternal separation).
3.1.7. Animal models for drug addiction
Animal models for drug addiction can be classified into models forthe three stages: preoccupation/anticipation, binge/intoxication, andwithdrawal/negative affect stages (Koob and Volkow, 2010).
Animal models for the preoccupation/anticipation stage fit into two
categories: extinction-based and abstinence-based relapse models (Venniro et al., 2016). Extinction-based relapse models include drug-, cue-,context-, stress-, and withdrawal state-induced relapses (Alleweireldt
et al., 2001; Shaham et al., 2003), reacquisition (Bouton et al.,2012), and resurgence (Winterbauer and Bouton, 2011). Abstinence-based relapse models include forced abstinence and drug craving
incubation (Fuchs et al., 2006), adverse consequences-imposed abstinence (Cooper et al., 2007), and voluntary abstinence induced by introducing non-drug rewards (Caprioli et al., 2015). In addition, risky
and gambling choice tasks and those with reward/aversion conflicts canbe used to study the pathological oscillations underlying inappropriate,
impulsive and executive decision making in addicted states (Passecker
et al., 2019; Verharen et al., 2018).
Animal models of the binge/intoxication stage consist of intravenous
and oral drug self-administration (Ahmed and Koob, 1998), intracranial self-stimulation (Markou and Koob, 1992), conditional place preference (Sanchis-Segura and Spanagel, 2006), drug discrimination(Stolerman et al., 2011), and genetic models of high addiction susceptibility (Quintanilla et al., 2006). In addition, the drug taking in thepresence of aversive consequences model can be used to find pathological oscillations governing compulsive drug taking behavior (Vendruscolo et al., 2012).
Animal models of the withdrawal/negative affect stage include intracranial self-stimulation (reward decreases), conditional place aversion (Hand et al., 1988), measures of anxiety-like responses (e.g. theopen field and elevated plus mazes), and drug self-administration withextended access or in dependent animals (Ahmed et al., 2000).

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3.2.2. For clinical practice
The international 10–20 or 10–10 EEG recording systems are widelyused for standard diagnosis or study of a variety of neuropsychiatric disorders, including epilepsy (Nuwer et al., 1998). One of the advantages of EEG recordings is its time resolution. This enables fast oscillatory activities to be analyzed (typically 0.3–300 Hz). MEG recordings
have an even higher time resolution (in milliseconds). The frequency
spectrum density in each recording site and the relationships between
the recording sites (coherency, connectivity, causality etc.) are typicallyanalyzed. High-density EEG recordings (64–256 ch) increase spatial resolution and allow source imaging with even sub-lobar precision (Seecket al., 2017). This enables better spatial resolution for seizure focus
prediction with tomography. fMRI recordings give higher spatial resolution (in millimeters) but lower time resolution (in seconds) comparedto EEG recordings; they primarily utilize the blood-oxygen-level dependent contrast, which is complementary to EEG recordings. fMRI recordings can be used to investigate very slow oscillatory activities within andbetween brain regions. Invasive electrophysiological recordings on or in
the brain are required to find the seizure focus much more precisely or
the optimal location of DBS electrodes in the basal ganglia of PD patients.
3.3. Machine learning-mediated approaches for analysis
It is challenging to find disease or disorder-specific oscillation patterns in large-scale neuronal activity data. For example, unsupervised
learning techniques have been used to find significant coherent resting-state fluctuations and functional connectivity of resting-state fMRI
data (Khosla et al., 2019). Unsupervised methods like independent
component analysis (ICA) and principal component analysis (PCA) decomposition are also used to find latent variable models in fMRI data.
Deep learning methods such as convolutional neural networks and feedforward neural networks were used to successfully discriminate the fMRI
data of AD and schizophrenia patients from those of healthy control patients with 96.85 % and 85.8 % accuracy, respectively (Wen et al.,2018). These machine learning methods can also be applied for electrophysiological data to find disease-specific oscillation patterns in EEG or
intracranial LFP data from humans and experimental animals (Reardon,
2017). However, if the disease-specific oscillation model is extracted by
PCA, the model is so abstract that it cannot be interpreted well enoughto develop an intervention based on the analysis.
Recently, Gallagher and others successfully developed a new modeling algorithm for multi-region LFP recordings (cross-spectral fac

3.2. Neural activity recordings

3.2.1. For animal research
Large-scale brain dynamics recordings as LFPs are very powerful forinvestigating oscillatory activities across multiple brain regions (Hongand Lieber, 2019; Pesaran et al., 2018). Beyond single site recordings, multi-site recordings with silicon probes have allowed the geometry of oscillatory activities in the brain to be studied (Wise andNajafi, 1991). Linear 16–32 ch recording probes have been used tomap layer specific oscillations for example in the cortex (Minlebaev
et al., 2011). Recent CMOS-based probes (Neuropixel) enable up to960 ch high-density recordings on a single shank (Jun et al., 2017).
Multi-shank linear silicon probes (e.g. buz256) can capture two-dimen

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Seizure susceptibility is not currently titrated directly but estimated by
seizure occurrence frequency, which is time consuming and has high uncertainty due to sparseness. CSFA can be used instead to instantly quantify it once the pathological pattern has been modelled; instantaneousamount of a disease-specific oscillation pattern is quantified as a spectral
factor score (Gallagher et al., 2017; Hultman et al., 2018).
Given the hypothesis and the quantitative measure of pathological oscillatory network states, the following oscillotherapeutic strategies
would be effective to control epileptic seizures: (1) introducing oscillatory activity which resonates with and stabilizes non-seizure states (e.g.
theta oscillation in the HPC), (2) reducing the normal to ictal transition rate by reducing seizure susceptibility, especially during a seizure
prediction period, (3) inducing immediate transition from ictal to normal states by introducing a huge oscillatory disturbance on ictal hypersynchronous oscillations, (4) intervening to prevent or any intermittent
pathological oscillations which would induce maladaptation in neuralnetworks. These strategies could be effective for intervening in other oscillopathies with fast oscillatory state transitions, including PD and possibly for the psychotic attack of schizophrenia, for impulsive and addicted drug intake, and for an attack of PTSD.
For other oscillopathies with much slower underlying state transitions (e.g. GAD, depression, addiction craving, and AD), long-lasting
plastic changes in oscillating network dynamics would be required to be
induced by external oscillatory interventions. For example, on-demanddeletion and the continuous introduction of oscillations for negative andpositive feelings might improve depression symptoms.

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tor analysis, CSFA). This algorithm breaks the observed signal into factors defined by unique spatiotemporal spectral properties (a power orcross-spectral densities) (Gallagher et al., 2017). The critical thing isthat the factors are interpretable. Combined with a supervised-learningalgorithm, CSFA has revealed symptom specific oscillation patterns in
depression (Hultman et al., 2018).
4. Oscillopathy – the realistic view of pathological oscillatory
states and a strategy for oscillotherapeutics
Here we describe the pathophysiology of oscillopathies with an emphasis on epilepsy as a system of multistable dynamic oscillatory states.
We also provide a conceptual overview on how to intervene with pathological oscillations focusing on the control of epilepsy and epileptic
seizures in a time-targeted closed-loop manner.
4.1. Bistable or multistable circuit states

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4.1.1. Modelling concept and example of seizure model
Epilepsy is a network disorder which can be characterized bybistable or multistable oscillatory states (e.g. interictal and ictal states)
and the transitions between them (Kalitzin et al., 2019). Circuit-state
dynamics are determined by the stability of each state and probability oftransition between stable oscillatory states, which are supposed to be affected by the following factors: (1) network resonance, (2) resilience, (3)perturbation, sensory inputs (e.g. a well-timed pulse input), (4) neuromodulatory inputs, and (5) time spent in the state (Chang et al., 2018).
Stable oscillatory states include at least normal (interictal) and hypersynchronous ictal states. This concept has been validated in in vivo
animal and human recordings and in silico modelling studies. For example, theta frequency MS stimulation stabilized oscillatory activity inthe septo-hippocampal axis and decreased seizure susceptibility whereas
over 20 Hz MS stimulation induced transition from normal to ictal oscillatory state in rats (Fisher, 2015; Miller et al., 1994). Modellingstudies have successfully established realistic behaviors of epileptic networks, which resemble network states including, normal states, pre-ictal
recruitment, epileptic seizures, and post-ictal suppression (Bauer et al.,2017; Jirsa et al., 2014). The seizure models can recapitulate oscillatory state transitions including the onset, evolution, and terminationof epileptic seizures. They help to explain mechanisms underlying thestate transitions and they may enable upcoming seizures to be predicted.
They may also enable secondary generalization and possibly sudden unexpected death in epilepsy to be explained (Kuhlmann et al., 2018).
Modeling studies have also explained the pathological oscillations of PD(Pavlides et al., 2015; Shouno et al., 2017).

4.2. Stimulation strategies
Pathological oscillations are intervened in an open- or closed-loop
manner in terms of how the stimulation is temporally delivered. Intrinsic structure of the neuronal networks can be utilized to maximize theeffect of stimulation.
4.2.1. Open-loop interventions
Open-loop intervention in the oscillotherapeutic context means introduction of external stimulation without the feedback of internal oscillatory activity (Fig. 2A). The external stimuli can be a sinusoidal
waveform or pulse trains. The open-loop intervention can be non-invasive (e.g. transcranial electrical stimulation: TES) or invasive (e.g. DBS).
The open-loop stimulation with a sinusoidal stimulus waveform caninteract with ongoing intrinsic network activities if appropriate stimulus intensity is provided (Fig. 2B). For example, transcranially applied alternating electrical stimulation can modify and entrain the membrane potentials of cortical neurons in rats (Ozen et al., 2010). Furthermore, intense TES at 1 Hz sinusoidal wave can phase-specificallyenhance alpha-band activity in the parietal cortex of healthy humansubjects (Vöröslakos et al., 2018). If properly applied, these resonance approaches can modulate the cognitive functions of humans (e.g.
enhancement of memory) (Hanslmayr et al., 2019; Reinhart andNguyen, 2019). The continuous application of high-frequency pulse
trains to the STN and the anterior nucleus of the thalamus (conventional
DBS) successfully improved symptoms of PD and drug-resistant epilepsy,
respectively (Deuschl et al., 2006; Li and Cook, 2018). However,open-loop interventions are less flexible in terms of temporal structurecompared with closed-loop interventions, which are discussed below.

4.1.2. Generation of hypothesis and quantification of circuit states
Modelling studies are not merely explanatory tools but also an instrument to generate a hypothesis. They can also provide readouts ofotherwise complex state indicators (Kalitzin et al., 2019). For example, CSFA (Gallagher et al., 2017), machine learning-assisted modelling of multi-site oscillatory networks, has revealed previously unknown pathological oscillations for depression (Hultman et al., 2018).
In a multistate model study, the seizure susceptibility of an instantaneous network state can be readout as a’ separatrix proximity’ forthe closeness to ictal transition (Petkov et al., 2018). This is measure of instantaneous distance between the current network state andthe threshold for the ictal (hypersynchronous state). Using this possible biomarker of seizure susceptibility, epilepsy is considered as a state
where the average network oscillatory state is close to the ictal threshold. Physicians or researchers can estimate the effectiveness of treatments or new intervention technique with this biomarker. This strategy is quite effective for predicting upcoming seizures, and for developing a new therapeutic technology in a very time-efficient manner.

4.2.2. Closed-loop interventions
Closed-loop interventions for oscillotherapeutics are brain stimulations based on intrinsic physiological signal feedback (e.g. LFP, EEG)
(Fig. 2A). The feedback information allows the stimulation to betime-targeted and on-demand stimulation. It also avoids overstimulationand prevents unwanted out-of-phase interactions.

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Y. Takeuchi and A. Berényi

Fig. 2. Brain stimulation techniques for intervening with pathological oscillations.(A) Schemas of open-loop and closed-loop interventions with epileptic seizures (pathological oscillation). Open-loop intervention provides pre-determined stimulus waveforms without processing recorded brain activities (e.g. EEG). Recorded brain activities for closed-loop intervention
are processed in rea-time, and the parameters of stimulus waveforms (timing, intensity, frequency etc.) are determined online. (B) A schematic showing of sinusoidal stimulation in an
open-loop manner and the typical responses of neural firing and intrinsic oscillations. Neural firings (spikes) and intrinsic oscillations are entrained to the externally applied stimulus
frequency (resonance approach). (C) Conceptual examples of closed-loop interventions for the destruction and restoration of intrinsic oscillations. Counter-phase stimulation (e.g. electricalstimulation) provides the opposite effect of intrinsic oscillation to destroy an arbitrary oscillation. In contrast, in-phase stimulation can restore decreased oscillation by enhancing thesummation of intrinsic oscillation. (D) A stimulation strategy for providing a widespread oscillatory effect externally by stimulating a target brain structure that provides diffuse axonal
projections with fast synaptic transmissions (e.g. the medial septum). (E) A stimulation strategy for intervening with a huge internal oscillation (e.g. epileptic seizures). Stimulation of abottleneck structure (choke point) of the internal oscillation can effectively intervene with it (e.g. the entorhinal cortex).

Closed-loop stimulation can reduce the side effects of excessive andunnecessary stimulus delivery. Chronic stimulation in a non-responsive,
open-loop manner may be unnecessarily excessive. Unnecessary stimulation can introduce adverse effects by disturbing physiological oscillations in the brain both by spatial on and off target effects. For example, chronic stimulation of the HPC may disrupt normal physiologicaloscillation and thereby cognitive functions including learning and memory. Continuous strong electrical stimulation on the scalp can introduce
pain sensations via peripheral nerve stimulation. In addition, chronic
electrical stimulation of the limbic structures (including the HPC andAMY) could induce pro-seizure effects called kindling (McIntyre andGilby, 2009). Importantly, an excessive continuous stimulus could induce accelerated habituation or rebound symptoms in patients (Pilitsis
et al., 2008; Shih et al., 2013). Patients instructed to turn on DBS
in an on-demand manner for essential tremor had longer effects thanopen-loop continuous stimulation (Kronenbuerger et al., 2006).

Closed-loop stimulation could introduce much higher therapeutic effects for neurological and psychiatric disorders. The first closed-loop
configuration is ‘closed-loop responsive’ stimulation, in which pre-determined stimulus pulses are delivered only when the stimulus is necessary (on-demand stimulation). In this configuration, physiological signals are continuously monitored to automatically trigger stimulationin an on-demand manner. For on-demand control of epileptic seizures,
we have revealed that transcranial applied electrical stimulation triggered by electrographically monitored seizures can effectively shorten
the duration of petit mal seizures in rats (Berényi et al., 2012). The
closed-loop seizure suppression remained effective at least for months(Kozák and Berényi, 2017). Responsive neurostimulation (the RNS
system) is approved for human application by the U.S. Food and Drug
Administration (FDA) as an adjunctive therapy for medically intractablepartial seizure patients (Morrell, 2011). Seizure or seizure-predicting
neuronal activity is monitored via depth and/or subdural strip leads

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ity like epileptic seizures. For example, partial seizures in the HPC occasionally secondary generalize, which may be a cause of sudden unexpected death in epilepsy (SUDEP) (Bone et al., 2012; Massey et al.,2014). The subiculum and the entorhinal cortex are bottleneck structures (choke points) from the HPC to the neocortex. Interventions onthese structures can effectively suppress the secondary generalization of
HPC-origin seizures (Lu et al., 2016; Wang et al., 2017). The thalamus and the STN are choke points in post-stroke epilepsy and absence
epilepsy, respectively (Paz and Huguenard, 2015).

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and electrical stimulation is delivered through these leads in an on-demand manner in the RNS system. For example, HPC and cortical activities are monitored and intervened via the depth and strip leads, respectively. Closed-loop responsive stimulation can be controlled by behavioural oscillations as well. Koganemaru and others have reported thatwalking rhythm triggered TES of the primary motor cortex in stroke
patients could improve their gain disturbance (Koganemaru et al.,2019).
The second closed-loop configuration for brain stimulation is‘closed-loop adaptive’ stimulation, in which input physiological variables gate output variables for stimulation. For example, the power ofbeta oscillation in STN LFP determines the intensity of DBS on the STN
for PD patients (Bouthour et al., 2019). The relationship between input and output variables is fixed or programable. Adaptive closed-loop
brain stimulation has been suggested for controlling neurological andpsychiatric disorders (epilepsy, movement disorders, AD, depression,and obsessive-compulsive disorders) (Hoang et al., 2017; Provenza etal., 2019).
The third important nature of closed-loop stimulation is ‘phase-targeting’ stimulation. Theoretically, phase-targeting electrical stimulationis very effective in the destruction of pathological oscillations and in
the restoration of reduced physiological oscillations (Fig. 2C).
Counter-phase stimulation destructs pathological oscillations andin-phase stimulation restores reduced physiological oscillations. Practically, precisely-timed phase-targeting stimulus delivery over ongoing
pathological oscillations has been proved to be essential for theclosed-loop intervention of epilepsy and PD (Berényi et al., 2012;
Holt et al., 2019). In addition, HPC LFP phase-specific fornical stimulation has been proposed to improve the cognitive functions of AD patients (Senova et al., 2018). This is because temporal organization oftheta and gamma oscillations in the HPC are thought to be importantin memory encoding and retrieval (Colgin, 2016). Dejean and others
have shown that the phase-specific optogenetic excitation of PV-positive interneurons in the PFC over frontal 4 Hz oscillation bidirectionallycontrols fear behavior of auditory cue conditioned mice (Dejean et al.,2016).

5. Oscillotherapeutics – embodiment for distinct modalities

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Here we provide an overview of several brain stimulation technologies with different modalities that can be employed for oscillotherapeutics. In addition to the time-targeting nature of closed-loop configurations, brain stimulation technologies offer space-targeting. Combining less aversive time- and space-targeting enables oscillotherapeutics to provide therapeutic options distinct from pharmacological treatments, which are easier in terms of their application but have sustained
and off target effects. We discuss deep brain stimulation (DBS), transcranial electrical stimulation (TES), transcranial magnetic stimulation(TMS), transcranial focused ultrasound stimulation (tFUS), and optogenetic stimulation for the space-targeting.
5.1. Deep brain stimulation
The main advantages of DBS for oscillotherapeutics are its focality
and efficacy (Kringelbach et al., 2007). Stimulus leads are directly inserted into the brain. Electrical stimulation can be limited to the target
structure and the stimulation energy directly activates nearby neurons
and axonal fibers without shunting via the skull. However, one of drawbacks of DBS is its invasiveness. There are risks of infection and bleeding
because of the electrode insertion. Although DBS is basically reversible,
the insertion causes microlesion along the electrode track from the surface to the target region. Thus, if the target region is superficial, transcranial stimulation may be considered.
DBS electrodes are precisely located with stereotaxic surgery while
stimuli are delivered to validate its effects on symptoms especially for
motor disorders. DBS stimulation can be provided in a closed-loop responsive manner to achieve time-targeting (Bouthour et al., 2019).
DBS stimulations are usually provided as pulse trains and they can be delivered chronically, intermittently, or precisely synchronized with electrophysiological or behavioural signals in a closed-loop manner. The
stimulus parameters include frequency, pulse width, pulse amplitudeetc. These parameters have been titrated by physicians empirically, butthey can be guided by pre-clinical studies using optogenetics (Creedet al., 2015). The stimulation parameters can be updated online using
closed-loop adaptive techniques. For example, dynamic changing of amplitude in response to beta-band power in the STN for PD (Bouthour etal., 2019; Provenza et al., 2019). DBS has already been used for PD,
drug-resistant epilepsy, treatment resistant depression, and drug addiction (Deuschl et al., 2006; Li and Cook, 2018). DBS for AD, schizophrenia, GAD, and PTSD are under investigation (Bina and Langevin,2018; Krack et al., 2010). DBS acts rapidly for movement disorders
but it takes time for depression treatment.

4.2.3. How to effectively introduce or disturb oscillatory activities
Introducing or disturbing oscillatory brain activities in an effective
way is essential for oscillotherapeutics. Utilizing intrinsic circuit structures in the brain is an important factor for effective interventions. Stimulation of a hub-like structure is effective upon introducing widespread
oscillatory activity into the brain (Fig. 2D). If the target brain region
has diverging projections to several destination brain regions, oscillatory activity applied to the target region can spread to the destination
brain regions via axonal projection and synaptic transmissions. The applied stimulation frequency should be within the frequency propertiesof the neuronal circuits. For example, the MS is in the midline of thebasal forebrain and projects to the bilateral HPC in a diverging way. This
pathway is mainly mediated by fast GABAergic synaptic transmissions(Dutar et al., 1995). The MS and HPC are highly coherent in both
physiological and pathophysiological conditions. The electrical or optogenetical stimulation of the MS is transmitted to the bilateral HPC at exactly the same frequency that is applied within the delta to gamma-band
range (Sinel’nikova et al., 2009; Zutshi et al., 2018). For humanapplication, the thalamic stimulation for refractory epilepsy, the fornical stimulation for AD, and the medial forebrain bundle stimulation for
depression all utilize this principle for DBS (Dandekar et al., 2018; Li
and Cook, 2018; Mirzadeh et al., 2016). In addition, both TES andtranscranial magnetic stimulation (TMS) of the PFC for treatment of depression and drug addiction also utilize this principle, because the PFC
has widespread synaptic connections to the limbic brain structures (Ferenczi et al., 2016; Loo et al., 2012; Noda et al., 2017).
The intervention of a bottle-neck relay station in the brain is effective for intervening with the propagation of huge oscillatory activ

5.2. Transcranial electrical stimulation technologies
TES is a non-invasive brain stimulation technique. It is a safe andreversible adjunctive therapy because its stimulus electrodes are located outside the skull. TES technologies are classified into ECT andnon-convulsive therapy. Although ECT on the PFC is one of the effective therapies for depression (and is still used for schizophrenia
in developing countries), we focus here on non-convulsive TES. Thisis because of its therapeutic potential for the on-demand control of

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Recently, a new tACS configuration has emerged, the so-called
high-definition (HD-tACS). Stimulus focality is dramatically increased in
HD-tACS via having one stimulating electrode on the target structure
surrounded by several anti-phase returning electrodes. Several corticalregions can be independently stimulated with distinct oscillatory stimulus waveforms by using this stimulation technology. Reinhart has artificially synchronized and desynchronized the human medial frontal cortex and the lateral PFC in theta-band frequency, and successfully modulated executive functions using HD-tACS stimulation (Reinhart, 2017).
Reinhart and Nguyen also found that the forced coupling of the PFC
and the temporal cortex in theta frequency improved working memorytask performance in aged adults (60–76 years of age) (Reinhart andNguyen, 2019). HD-tACS can be delivered in a closed-loop manner.

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many symptoms of neurological and psychiatric disorders, combinedwith closed-loop time-targeting nature. The focality of TES is not as goodas DBS or TMS because of its transcranial nature. However, its diffuse
mild modulation over the cortex may be an advantage for intervention(e.g. desynchronization) of pathological oscillations in widespread cortical regions for example absence seizures (Berényi et al., 2012; Kozák
and Berényi, 2017). Furthermore, the focality of TES has improved via
recent advances in the technologies we discuss below. Traditionally, thestimulus intensity of non-convulsive TES has been limited to up to 2 mA
and it has been used for neuromodulation.
However, we recently found that this amount of stimulus intensity is
not enough to directly induce large enough electrical fields (1 mV/mm)
that in turn reliably induce action potential in the intracranial space, because of the shunt effect of the skull and skin (Liu et al., 2018). Thus,
we have developed a new TES technology (Intersectional-Short Pulse(ISP) stimulation) by which electrical stimulation can be transcranially
focused in any intracranial space, enabling much higher electrical currents (Vöröslakos et al., 2018). Wearable or implantable devices for
closed-loop, focused TES intervention will be available in the future because TES device can be implemented in smaller devices than TMS andtFUS technologies (see Section 6).

5.2.3. Temporal interference stimulation
Temporal interference (TI) stimulation is a newly developed TES
technique that enables deep brain stimulation without electrode insertion (Grossman et al., 2017). TI stimulation utilizes the temporal interference between two electrical fields with alternating vectorial directions in similar but slightly different, over-kilo Hertz frequencies. For
example, Grossman and others applied 2 kHz (f1) and 2.01 kHz (f2) alternating current stimulations on the heads of mice. Depending on thealignment of the electrodes, the superposition of the two electric fields
inside the brain resulted in an electrical field at the average frequencyof f1 and f2, whose envelope was modulated at the frequency of delta
f (10 Hz). The 10 Hz modulated envelope entrained the action potentials of the neurons inside the brain, whereas the very high frequency
oscillating (>2 kHz) electrical fields did not cause any changes in themembrane potentials of cortical neurons because of intrinsic low-passfiltering properties of the neuronal membrane. They also showed that TI
stimulation could transcranially induce cFos (immediate early gene) expression in the hippocampal neurons of mice, but not in neurons in theoverlaying cortex.
The TI stimulation is an innovative technique, but only its proof ofconcept has been established so far, especially because it does not address the bottleneck of intensity dependent peripheral side-effects establishing its efficacy with non-human primates or healthy human volunteers is required before possible clinical application. The spatial resolution and maximal depth for effective stimulation will be dependent on
the number and alignment of electrodes on the scalp. One issue could be
the possible off-target effects of very high frequency electrical fields overlarge brain areas. Strong kHz-frequency electrical fields can block the
propagation of compound action potential in peripheral nerves (Kilgore
and Bhadra, 2014). In their experiments, the 2 kHz electrical stimulation with hundreds microampere order did not induce any acute physiological effects on the brain. Further experiments will be necessary for
determining the upper limit of the electrical field to ensure that any conduction block on axons in the brain is not induced. Chaieb and others
have reported that very high frequency electrical fields (1–5 kHz) could
induce neural plasticity similar to that which can be evoked by anodal
direct current stimulation (Chaieb et al., 2011). The long-term effects
of kHz stimulation of TI should be studied in future. Temporal resolution
of TI simulation has a limitation because the introduction of kHz electrical fields in short ramp-up times induces the transient non-spatially focused activation of neurons, whereas slow ramp-up does not. Because ofthis limitation, closed-loop TI with time precision (e.g. phase-targetingstimulation) is not feasible. Therefore, TI stimulation seems preferable
to applications for inducing plasticity in non-cortical deep brain regions.

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5.2.1. High definition transcranial direct current stimulation
tDCS is used for inducing plastic changes by introducing subthreshold membrane potentials in the neurons in the cerebral cortex. Classical tDCS employs two large electrodes (25–35 cm2) for the stimulation.
tDCS induces subthreshold depolarization of cortical neurons below theanodal electrode, and hyperpolarization below the cathodal electrode.
Repeated tDCS conducted once a day for five or more consecutive days,
can induce long-lasting effects on motor performance, depressed mood,and other functions for one month or more after stimulation (Buch etal., 2017; Loo et al., 2012). Efforts have been made to increase thefocality of tDCS by reducing the size of the relatively large stimulus
electrode placed over the target area, by increasing the size of the return electrode, and/or by placing the return electrode at another location other than the scalp (for example, the neck, shoulder, and arms, orknee).
Alternatively, an electrode configuration has been developed to optimize stimulation focality on the basis of the modelling of electricalfield strength. The so-called high definition tDCS (HD-tDCS) is one ofthese approaches (Nitsche et al., 2015). Relatively small electrodes
are placed in the vicinity of the stimulation electrode for this approach(Datta et al., 2009). Since the distance between the respective electrodes is relatively short, shunting is enhanced relative to the more conventional electrode arrangements. Therefore, current density has to berelatively high to obtain similar effects as large electrode pads. Studies have revealed that HD-tDCS treatment has alleviated symptoms of
epilepsy and pain perception (Castillo-Saavedra et al., 2016; Meiron
et al., 2019).
5.2.2. High definition transcranial alternating current stimulation
Transcranial alternating current stimulation (tACS) is relatively a
newly developed stimulation technique that non-invasively modulates
cortical excitability and activity. While tDCS induces neuroplasticity via
the constant polarization of neuronal membrane potentials with the application of a tonic subthreshold direct current, tACS is thought to affectneuronal membrane potentials by oscillatory electrical stimulation witha specific frequency (Nitsche et al., 2015). The stimulation used fortACS and its duration are quite similar to tDCS. tACS at a normal stimulus frequency (1–100 Hz) does not induce any plasticity but it interacts
with the intrinsic oscillatory activities in the brain (Antal et al., 2008).
However, tACS at a much higher frequency (140 to low kHz range) may
induce neuroplastic excitability alterations (Antal et al., 2017).

5.2.4. Intersectional short pulse stimulation
We have recently developed a new TES technology, by which stimulus induced electrical fields can be focused in any region of the11

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ing magnetic field gives rise to a companion electric field that induces
electric currents in nearby conductive structures (Hamada and Rothwell, 2015). A large pulse of current in the external stimulating coilgenerates a rapidly changing magnetic field that rises to (and fallsfrom) 1 T or more within 1 ms. The design of the external stimulating coil affects the distribution of the induced field in the brain; thespread and depth of conventional figure-8 stimulating coils are 5 cm2
and 1.0–3.5 cm, respectively (Deng et al., 2013). The main target of
TMS is the cortex, but H-coil and double cone coils also enable the stimulation of deeper structures.
The main advantage of TMS is its high titer stimulation. Its inductedcurrents in the intracranial space are strong enough to directly evoke
the action potentials of cortical neurons without resonance (stimulation of the motor cortex can evoke muscle movements). rTMS of the
PFC achieves better or comparable efficacy than ECT for depression andit has less cognitive side effects. The time-resolution of TMS is goodenough to be combined with closed-loop configurations. Major drawbacks of TMS are its cost and size. Due to the physical constrains of coilsize, TMS instruments cannot be implanted or wearable. TMS treatments
can only practically be possible in hospitals.

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brain (Vöröslakos et al., 2018). The spatially-targeted TES has beenachieved by spatiotemporally rotating Intersectional Short Pulse (ISP)
stimulation (2.5–10 µs duration, 5–50 µs pause, depending on the number of electrode pairs) (Fig. 3A). This method exploits the integration time constant of the neuronal membrane (5–20 ms), a mechanismthat can temporally integrate multiple consecutive electrical gradientswith similar vector directions (Fig. 3A). Because of this, ISP stimulation could transcranially focus a strong enough electrical field on a target brain region to directly induce or inhibit action potentials (> 1 mV/mm) without placing too much current densities on the scalp (i.e. it isless painful). Spikes of HPC neurons could successfully be modulated
in a hemisphere specific manner with ISP stimulation. Furthermore, the
1 Hz ISP stimulation phase- and hemisphere-specifically modulated amplitude of alpha-band activities of EEG in healthy volunteers.
The major advantages of ISP stimulation are: (1) Focality. ISP stimulation can focus on a small brain region. This focus can be further improved by placing a number of electrode pairs on the scalp; (2) Time-resolution. It can be easily implemented with a closed-loop configuration with millisecond precision. Phase-targeted closed-loop stimulationis possible; (3) Intensity. Currents as high as ten-sixteen mA can be used
(an order of magnitude larger than conventional TES) because the current density on each electrode is similar to that of conventional TES;
(4) Bilateral stimulation. The same effects (excitation or inhibition) canbe applied on both hemispheres simultaneously by aligning electrodes(unlike conventional TES, which induces opposing anodal-cathodal effects). This is because direction of electrical fields along dendrite-axon
axis on neurons determines whether the electrical fields activate or inhibit the target neurons (Liu et al., 2018), and ISP can induce symmetrically directed electrical fields on the both hemispheres (Fig. 3B);
(5) Versatility. Several distinct stimulus waves can be employed independently for example to introduce distinct stimulus phase difference todifferent brain regions (Fig. 3B). ISP stimulation can be combined with
tDCS, tACS, and even TI stimulation in principle; and (6) Simplicity. The
ISP stimulation device can easily be miniaturized to be implantable or
wearable. This enables the long-term control of neurological and psychiatric disorders, even for domiciliary care (Fig. 4B). ISP stimulation
could enable non-invasive on-demand closed-loop control with spaceand time-targeted brain stimulation for neurological and psychiatric disorders.

5.4. Transcranial focused ultrasonic stimulation

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Low-intensity tFUS is an emerging brain stimulation technology thatmodulates mammalian brain activity (Fomenko et al., 2018). Relatively low intensity (<3 W/cm2) and low frequency (0.25–0.5 MHz) ultrasound stimulation is used for human neuromodulation studies. Stimulation parameters (e.g. sonication duration) and intensity determine
whether a stimulus has excitatory or inhibitory effects. The mechanisms
by which low-intensity ultrasonic stimulation modulates neuronal activity have been poorly understood. Thermal and non-thermal mechanisms
have been proposed, but thermal mechanisms are negligible because
low-intensity tFUS does not cause a significant rise in tissue temperatureduring sonication (<0.1 °C). Direct effects on neuronal membrane andion channels have been suggested as non-thermal mechanisms (Plaksin
et al., 2014).
The major advantage of tFUS is its focality even for deep brain structures (however, compensation of bone scattering is challenging). Human and non-human primate studies have shown that the stimulus focus can be as small as 2–5 mm and tFUS can focus on (for example)
the basal ganglia without affecting the cortex (Legon et al., 2014).
In addition, tFUS can be delivered in a closed-loop manner (Fig. 4A).
Miniaturization of the transducer and amplifier will be required for implantable or wearable devices. Implantable transcranial ultrasonic devices for mice have been reported, but none for humans yet (Li et al.,2019). tFUS can transiently open the blood brain barrier and it can

5.3. Transcranial magnetic stimulation

TMS is non-invasive brain stimulation technique that has been used
for modulating symptoms of depression, schizophrenia, addiction etc.
(Diana et al., 2017; Farzan et al., 2012; Fox et al., 2012). TMS
employs the principle of electromagnetic induction, in which a chang

Fig. 3. Intersectional Short Pulse stimulation: a focused transcranial electrical stimulation technology.(A) Principle of Intersectional Short Pulse (ISP) stimulation. Multiple anodal andcathodal electrode pairs are located on the skull or scalp. Electrical stimulation was temporally interleaved at 5–25 µs and rotated within the electrode pairs. The electrical field generated
by the stimulation is focused to an arbitrary region of the brain. (B) Expansion of ISP technology for multiple focusing points with two distinct stimulus waveforms (green and magentain the left). Depending on electrode alignment and stimulus polarity, multiple brain regions can be stimulated using distinct stimulus waveforms causing the same effect (excitatory or
inhibitory) on both hemispheres (right). Green and magenta arrows indicate current flows from anode to cathode. Black arrows indicate phase differences (temporal shifts) between the
distinct stimulus waveforms.
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Fig. 4. Future direction and engineering challenges.(A) Pre-clinical research and development of closed-loop interventions for neurological and psychiatric disorders in the near future.
Efficient algorithms should be developed for real-time detection of specific pathological oscillation patterns with large-scale data streams. Several brain stimulation modalities (e.g. electrical, ultrasonic, and optical) are investigated for efficient transcranial stimulation. (B) The development of wearable devices for the closed-loop intervention of neurological and psychiatricdisorders. The real-time decoding of specific features of chronic symptoms from resting state EEG (rs-EEG) will be much more demanding on computation. Parallelization of processing
units will be necessary for faster computation and lower power consumption. The devices need to be miniaturized to become wearable or implantable.

be used for both time- and space-targeted drug delivery for the central
nervous system following intravenous administration of nanoparticle- or
microbubble-caged drugs (Landhuis, 2017).

graphic seizures in mice (Krook-Magnuson et al., 2013). Carlson andothers showed that cortical slow oscillation-timed gamma frequency optogenetic activation of the PFC to thalamic pathway alleviated depression-like behavior in mice (Carlson et al., 2017). Major drawbacks of
optogenetics as a brain stimulation technique for humans are gene introduction and invasiveness. Virus-mediated gene transduction and theinsertion of optical fibers into the brain is not easily justified in terms ofrisk versus benefit. Magnet or radio-wave sensitive genetically coded actuators may enable the transcranial manipulation of defined neural circuits without optical fiber insertions, but gene manipulation is still required. However, Optogenetics is a very powerful and useful techniquefor basic oscillopathy research.

5.5. Optogenetics

Optogenetics is an innovative technology for the cell-type-specificmanipulation of neuronal excitability with millisecond precision. Causal
relationships between neuronal activities and natural behaviors havebeen extensively explored in neuroscience fields using optogenetics(Grosenick et al., 2015). The major advantages of optogenetics as abrain stimulation technique come with the genetically coded nature of
its actuators (opsins). This genetic nature combined with various promoters and viral vector-mediated gene transduction enables cell-typeand neural pathway-specific, and even activity-dependent opsin expression in arbitrary brain regions (Kim et al., 2017). Opsin expression
can be pharmacologically controlled via the Cre-estrogen receptor or Tet
on/off system. The manipulation of neuronal excitability can be bidirectional with fine temporal resolution. For example, channelrhodopsin-2
and iC++ can respectively be used for blue light-mediated excitationand inhibition with millisecond precision.
The pathophysiology of neurological and psychiatric disorders andpossible intervention strategies for them have been explored using animal models with closed-loop optogenetics. For example, Krook-Magnuson and others showed that seizure-triggered on-demand optogenetic inhibition of HPC pyramidal neurons suppresses HPC electro

6. Engineering challenges and future directions
In addition to the pathophysiological aspects of oscillopathies andbrain stimulation technologies themselves, technical implementation
challenges the clinical applications of closed-loop brain stimulation
should be addressed. Here we focus on the challenges of implementing closed-loop ISP stimulation for the real-time control of refractory
epilepsy and beyond.
6.1. Recording and real time processing of pathological oscillations
Pathological oscillations need to be intervened in real-time for the
online control of neurological and psychiatric disorders. At basic research levels, we previously developed an analog electrical circuit sys

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sive calculations as the modelling process. A reduction in the dimension and complexity of the established model may be required for online intervention. Superfast, massively parallelized graphical processing
unit (GPU) computing would be useful to implement real-time online
closed-loop interventions for research purposes.

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tem for the real-time detection and phase-specific intervention of epileptic seizures (Berényi et al., 2012). This system is very fast and has
virtually no delay. However, an analog electrical circuit system is not
flexible enough to be the best solution for developing new algorithms oreven for tuning the detection parameters of existing algorithms. Analog
electrical circuit systems are not even size efficient.
Therefore, we have developed a digital seizure detection system using a real-time digital signal processor unit and a 256 ch multiplexedbiosignal amplifier (Berényi et al., 2014; Kozák and Berényi, 2017).
Harnessing its 100 MHz digital signal processors (DSPs), we could implement a 24 h real-time closed-loop TES system with up to eight subjects (rats). Each subject has 30 ch recording sites. The time resolution of the system is 2 ms, but it is configurable with a trade-off between the number of recording sites and the number of calculations
for each subject. The graphical programing environment for the system enabled us to utilize many digital signal processing functions (arithmetic, filtering, root-mean-square, power spectrum and coherency calculation, timer, counter, logic gates etc.) to implement virtually any
simple detection algorithm from multi-dimensional time series. While aclosed-loop control program is running in the system, any parameters ina detection program can be turned via any external programming environment that supports ActiveX™ controls including MATLAB, Visual
Basic, Visual C++, LabView, Python etc. Other groups have also reported seizure detection systems; for example, Armstrong and others reported a MATLAB-based system (Armstrong et al., 2013). The Open
Ephys-based closed-loop system is also getting popular in field in systems neuroscience (Siegle et al., 2017). There is some intervention delay for closed-loop in the system because signal processing calculations
are conducted in the host PC. They are dependent on the data transfer
speed of serial ports (means of 11 ms via USB2.0 and 8 ms via USB3.0).
This delay is planned to be reduced to be less than 1 ms by developing a PCIe acquisition board in future (https://open-ephys.atlassian.net/wiki). Phase-locked (targeting) stimulation is essential for efficient intervention in pathological oscillations. New algorithms (for instantaneous
phase-calculation, for example) will be useful and can be implemented
in the above mentioned closed-loop system for efficient pathological oscillation intervention (Mansouri et al., 2017).
The real-time prediction of seizures is more demanding than detecting seizures themselves because of smaller signal-to-noise ratios anduntypical feature changes, but prediction is more valuable than detection as it enables prevention (Kuhlmann et al., 2018). Recruiting
larger number of time series data for analysis, calculating coherence or
causality matrices, entropy, phase-amplitude coupling, and/or loss of resilience could all be useful to increase detection power and decrease
false positive detections. They could enable the detection of a seizure
predictor specific network activity pattern as three-dimensional template matching (Fig. 4A) (Chang et al., 2018). The real-time detectionof the symptom-specific network activities of psychiatric disorders canbe as (or more) demanding as seizure prediction because its oscillatory
representations are not supposed to be as evident as epileptic seizures.
They are typically subtle and distributed to many brain regions (Fig.
4B) (Carlson et al., 2017; Hultman et al., 2016, 2018). This meansthat much faster algorithm and computation may be required for psychiatric disorders than for epilepsy.
Another possible issue related to interventions for predicted seizures
and psychiatric disorders is that their specific oscillatory patterns are
so complex. It would not be very efficient to find these patterns via exploratory analyses by the researchers themselves. As we have alreadydiscussed earlier in this paper, machine learning-mediated modellingof oscillatory network activities (e.g. CSFA) would be useful to identify them instead. Although precise offline modelling is a time-consuming process even with powerful computers, online detection of specific oscillatory patterns based on the established model (real-time brain
state classification) would be feasible because it does not require inten

6.2. Stimulation: precise localization for targeting a seizure (or symptom)
focus in the brain

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Increase of number of pairs of stimulus electrode pairs is importantfor further improvement of spatial resolution of ISP stimulation (andthis is the case for TI stimulation and tFUS as well). A specialized headset with multiple EEG and stimulus electrodes is required for transcranial closed-loop intervention with ISP stimulation (Fig. 4B). Intracranial implantation of the stimulus electrodes will increase the efficacy
and focality at the expense of non-invasiveness. The alignment of stimulus electrodes should be tailored for the individual needs of each patient, especially for focal seizures. The target brain region should bedetermined via high-density EEG-mediated functional tomography andthe long-term video monitoring of seizures. Brain structure imaging willbe also required for designing of the ISP stimulation target. Althoughelectrical artifacts of ISP stimulation are smaller than those of conventional TES, feedforward removal of gross artifacts from applied currents is required (Kohli and Casson, 2019; Vöröslakos et al., 2018).
Ready-to-use simulation environments for electrode alignments and artifact predictions should be developed for the clinical application of ISP
stimulation.
The optimization of stimulation parameters (stimulus frequency, duration, intensity etc.) is critical for alleviating symptoms effectively.
This optimization has been conducted by physicians empirically, but it
is time-consuming. Deep learning technology is suggested to find optimal DBS stimulus parameters for movement disorders for the effective
real-time classification of brain states (a closed-loop intelligent implementation of adaptive DBS) (Neumann et al., 2019). This approach
could be implemented in future for optimizing closed-loop ISP stimulation to control epilepsy and psychiatric disorders as well. Supervised
and reinforced learning would be useful for initial parameter settingsand dynamic optimization during ongoing intervention treatments, although artificial intelligence (AI) medicated brain stimulation is currently cleared by the FDA
6.3. Device implementation- miniaturization, power supply, and IoT in the
5 G eraThe implementation of ISP stimulation for implantable or wearable
devices requires the miniaturization of each unit for recording, processing, and stimulation, which we are working on. Detection algorithms
should be simplified and optimized to be implemented into a microprocessing unit for example a field-programmable gate array (FPGA),
although it requires low-level programming skills of experienced engineers. Massive parallelization will be required to ensure real-time
computation in a small device with limited clock rate without significant delay. This will also improve power efficiency (Hirano et al.,2015). The use of bionic chips optimized for machine learning withneuronal data (Qiao et al., 2015) would be useful for much faster
mobile devices as implemented in iPhone X (Apple A11) or Intel’s
Nervana Neural Network Processors. Even quantum computing processors (e.g. Google’s Sycamore processor) might be implemented in medical devices in the future (Arute et al., 2019). Many implantableor wearable medical devices will be always connected via the Internet of Things (IoT) in the 5 G or 6 G era. They will have large highly
reliable data transfer capabilities with negligible delay. Computation
for closed-loop interventions may be outsourced to external supercomputer servers via highly reliable bidirectional data streams in those eras.

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The power supply for implantable or wearable devices needs to be addressed. Miniaturized rechargeable batteries with large capacities are
required. Contactless recharge stations (via magnetic induction for example) may be available everywhere in the future at least in big cities(Mickle et al., 2019).

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Author Contributions
Y.T. and A.B. conceived the idea. Y.T. wrote the original draft and
A.B. reviewed and edited it. Y.T. prepared the figures and A.B. supervised this work.
Declaration of Competing Interest
A.B. is the owner of Amplipex Llc and Neunos Ltd in Szeged, Hungary, manufactures of signal-multiplexed headstages and neurostimulator devices.
Acknowledgments

References

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This work was supported by KAKENHI (18KK0236, 19H03550,
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EU-FP7-ERC-2013-Starting grant (No. 337075), EU H2020 No. 739593,
Momentum program I and II of the Hungarian Academy of Sciences,
EFOP-3.6.1-16-2016-00008 - National Research, Development and Innovation Office, Hungary, and 20391-3/2018/FEKUSTRAT of the Ministry of Human Capacities, Hungary to A.B.

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