Biomarkers and Stimulation Algorithms for Adaptive Brain Stimulation

Abstract

The goal of this review is to describe in what ways feedback or adaptive stimulation may be delivered and adjusted based on relevant biomarkers. Specific treatment mechanisms underlying therapeutic brain stimulation remain unclear, in spite of the demonstrated efficacy in a number of nervous system diseases. Brain stimulation appears to exert widespread influence over specific neural networks that are relevant to specific disease entities. In awake patients, activation or suppression of these neural networks can be assessed by either symptom alleviation (i.e., tremor, rigidity, seizures) or physiological criteria, which may be predictive of expected symptomatic treatment. Secondary verification of network activation through specific biomarkers that are linked to symptomatic disease improvement may be useful for several reasons. For example, these biomarkers could aid optimal intraoperative localization, possibly improve efficacy or efficiency (i.e., reduced power needs), and provide long-term adaptive automatic adjustment of stimulation parameters. Possible biomarkers for use in portable or implanted devices span from ongoing physiological brain activity, evoked local field potentials (LFPs), and intermittent pathological activity, to wearable devices, biochemical, blood flow, optical, or magnetic resonance imaging (MRI) changes, temperature changes, or optogenetic signals. First, however, potential biomarkers must be correlated directly with symptom or disease treatment and network activation. Although numerous biomarkers are under consideration for a variety of stimulation indications the feasibility of these approaches has yet to be fully determined. Particularly, there are critical questions whether the use of adaptive systems can improve efficacy over continuous stimulation, facilitate adjustment of stimulation interventions and improve our understanding of the role of abnormal network function in disease mechanisms.

Cellular, molecular, and clinical mechanisms of action of deep brain stimulation—a systematic review on established indications and outlook on future developments

“…The electrical excitability of neural tissue around the electrode and the effects of stimulation are determined by several factors: (i) the proportion of gray and white matter structures (i.e., cell bodies or axons; Nowak & Bullier, ; Ranck, ; Dostrovsky & Lozano, ; Udupa & Chen, ); (ii) the type of ion channels on the cell membrane of a soma or axon (Hoang et al , ); (iii) the diameter and degree of myelination of axons (Ranck, ; Nowak & Bullier, ; Gubellini et al , ; Carron et al , ); (iv) the orientation of axons in relation to the electrode (Ranck, ; Gubellini et al , ); (v) the distance of the stimulated structure to the electrode (Deniau et al , ); and (vi) the microenvironment (astrocytes and microglia; Song et al , ; Reddy & Lozano, ).…”

“…Thus, the current hypothesis of basal ganglia networks in PD is that the overall output to the thalamus and motor cortex is decreased and the circuit is caught in a hyper‐synchronized oscillatory state of entropy (Dorval et al , ; Anderson et al , ; Hoang et al , ). The efferent motor synapses of the STN during DBS are hypothesized to be depleted of their neurotransmitters and thereby filter low‐frequency oscillations (McIntyre & Anderson, ).…”

“…The efferent motor synapses of the STN during DBS are hypothesized to be depleted of their neurotransmitters and thereby filter low‐frequency oscillations (McIntyre & Anderson, ). Low‐frequency DBS (20 Hz) appears to increase the amount of synchronization, whereas high‐frequency DBS (> 70 Hz) suppresses such activity in the GPi (Brown et al , ), similar to the administration of levodopa (Dorval et al , ; Hoang et al , ).…”

Toward Electrophysiology-Based Intelligent Adaptive Deep Brain Stimulation for Movement Disorders

“…Interestingly, not only parkinsonian bradykinesia/ rigidity but also dopaminergic side effects are reflected in subthalamic LFP patterns, where dyskinesia symptoms were associated with increased low frequency (4-8 Hz) and narrowband gamma (60-90 Hz) synchronization [90,91], very similar to the activity seen during normal movement [53,62,92,93]. Moreover, additional pathophysiological hallmarks have been identified in PD and recent research suggests that indeed these different biomarkers may signal different aspects of parkinsonian motor signs [50]. The second most prominent LFP biomarker associated with parkinsonism is the presence of so-called high-frequency oscillations (HFO) at~250 Hz, which are not generally attenuated by dopaminergic medication, but rather are shifted toward higher frequencies to3 50 Hz [61,94].…”

“…Similarly, subthalamic DBS was demonstrated to suppress beta activity locally [43][44][45][46] and, again, the amount of suppression of local beta activity was correlated with improvement in parkinsonian motor signs [43,47]. The finding that a biomarker of concurrent parkinsonian motor sign severity can be recorded in real-time through the same electrodes that deliver the therapeutic stimulation has inspired the concept of a demand-dependent adaptive DBS paradigm [48], where the stimulation parameters are adapted directly according to the recorded electrophysiological parkinsonian symptom correlate, closing the loop from recording to stimulation [49,50]. Importantly, the presence of beta synchronization in the basal ganglia should not be mistaken as PD specific or pathological per se, as studies from other DBS patient groups, such as dystonia [51][52][53] and OCD [54][55][56], have reported peaks of beta activity in the basal ganglia.…”