scholarly journals The Emerging Role of Biomarkers in Adaptive Modulation of Clinical Brain Stimulation

Neurosurgery ◽  
2019 ◽  
Vol 85 (3) ◽  
pp. E430-E439 ◽  
Author(s):  
Kimberly B Hoang ◽  
Dennis A Turner
Keyword(s):  
Neural Networks ◽  
Brain Stimulation ◽  
Adaptive Systems ◽  
Brain Activity ◽  
Adaptive Modulation ◽  
Dynamic Control ◽  
Intracranial Stimulation ◽  
Electrode Position ◽  
Or Efficiency ◽  
Symptomatic Disease

AbstractTherapeutic brain stimulation has proven efficacious for treatment of nervous system diseases, exerting widespread influence via disease-specific neural networks. Activation or suppression of neural networks could theoretically be assessed by either clinical symptom modification (ie, tremor, rigidity, seizures) or development of specific biomarkers linked to treatment of symptomatic disease states. For example, biomarkers indicative of disease state could aid improved intraoperative localization of electrode position, optimize device efficacy or efficiency through dynamic control, and eventually serve to guide automatic adjustment of stimulation settings. Biomarkers to control either extracranial or intracranial stimulation span from continuous physiological brain activity, intermittent pathological activity, and triggered local phenomena or potentials, to wearable devices, blood flow, biochemical or cardiac signals, temperature perturbations, optical or magnetic resonance imaging changes, or optogenetic signals. The goal of this review is to update new approaches to implement control of stimulation through relevant biomarkers. Critical questions include whether adaptive systems adjusted through biomarkers can optimize efficiency and eventually efficacy, serve as inputs for stimulation adjustment, and consequently broaden our fundamental understanding of abnormal neural networks in pathologic states. Neurosurgeons are at the forefront of translating and developing biomarkers embedded within improved brain stimulation systems. Thus, criteria for developing and validating biomarkers for clinical use are important for the adaptation of device approaches into clinical practice.

Chaotic Neural Networks

2011 ◽  
pp. 275-281
Author(s):  
Emilio Del-Moral-Hernandez
Keyword(s):  
Neural Networks ◽  
Nervous System ◽  
Artificial Neural Networks ◽  
Adaptive Systems ◽  
Brain Activity ◽  
Theory And Practice ◽  
Chaotic Neural Networks ◽  
Non Linear ◽  
Artificial Neural ◽  
Complex Phenomena

Artificial Neural Networks have proven, along the last four decades, to be an important tool for modelling of the functional structures of the nervous system, as well as for the modelling of non-linear and adaptive systems in general, both biological and non biological (Haykin, 1999). They also became a powerful biologically inspired general computing framework, particularly important for solving non-linear problems with reduced formalization and structure. At the same time, methods from the area of complex systems and non-linear dynamics have shown to be useful in the understanding of phenomena in brain activity and nervous system activity in general (Freeman, 1992; Kelso, 1995). Joining these two areas, the development of artificial neural networks employing rich dynamics is a growing subject in both arenas, theory and practice. In particular, model neurons with rich bifurcation and chaotic dynamics have been developed in recent decades, for the modelling of complex phenomena in biology as well as for the application in neuro-like computing. Some models that deserve attention in this context are those developed by Kazuyuki Aihara (1990), Nagumo and Sato (1972), Walter Freeman (1992), K. Kaneko (2001), and Nabil Farhat (1994), among others. The following topics develop the subject of Chaotic Neural Networks, presenting several of the important models of this class and briefly discussing associated tools of analysis and typical target applications.

scholarly journals Applying a Sensing-Enabled System for Ensuring Safe Anterior Cingulate Deep Brain Stimulation for Pain

2019 ◽  
Vol 9 (7) ◽  
pp. 150 ◽  
Author(s):  
Yongzhi Huang ◽  
Binith Cheeran ◽  
Alexander L. Green ◽  
Timothy J. Denison ◽  
Tipu Z. Aziz
Keyword(s):  
Deep Brain Stimulation ◽  
Pain Relief ◽  
Brain Stimulation ◽  
Brain Activity ◽  
Anterior Cingulate ◽  
Chronic Therapy ◽  
Provide Pain Relief ◽  
Surgical Options ◽  
Effective Pain Relief ◽  
Deep Brain

Deep brain stimulation (DBS) of the anterior cingulate cortex (ACC) was offered to chronic pain patients who had exhausted medical and surgical options. However, several patients developed recurrent seizures. This work was conducted to assess the effect of ACC stimulation on the brain activity and to guide safe DBS programming. A sensing-enabled neurostimulator (Activa PC + S) allowing wireless recording through the stimulating electrodes was chronically implanted in three patients. Stimulation patterns with different amplitude levels and variable ramping rates were tested to investigate whether these patterns could provide pain relief without triggering after-discharges (ADs) within local field potentials (LFPs) recorded in the ACC. In the absence of ramping, AD activity was detected following stimulation at amplitude levels below those used in chronic therapy. Adjustment of stimulus cycling patterns, by slowly ramping on/off (8-s ramp duration), was able to prevent ADs at higher amplitude levels while maintaining effective pain relief. The absence of AD activity confirmed from the implant was correlated with the absence of clinical seizures. We propose that AD activity in the ACC could be a biomarker for the likelihood of seizures in these patients, and the application of sensing-enabled techniques has the potential to advance safer brain stimulation therapies, especially in novel targets.

scholarly journals Brain hierarchy score: Which deep neural networks are hierarchically brain-like?

2020 ◽  
Author(s):  
Soma Nonaka ◽  
Kei Majima ◽  
Shuntaro C. Aoki ◽  
Yukiyasu Kamitani
Keyword(s):  
Neural Networks ◽  
Image Recognition ◽  
High Performance ◽  
Deep Neural Networks ◽  
Recognition Performance ◽  
Brain Activity ◽  
Spatial Integration ◽  
Hierarchical Processing ◽  
Visual Areas ◽  
The Brain

SummaryAchievement of human-level image recognition by deep neural networks (DNNs) has spurred interest in whether and how DNNs are brain-like. Both DNNs and the visual cortex perform hierarchical processing, and correspondence has been shown between hierarchical visual areas and DNN layers in representing visual features. Here, we propose the brain hierarchy (BH) score as a metric to quantify the degree of hierarchical correspondence based on the decoding of individual DNN unit activations from human brain activity. We find that BH scores for 29 pretrained DNNs with varying architectures are negatively correlated with image recognition performance, indicating that recently developed high-performance DNNs are not necessarily brain-like. Experimental manipulations of DNN models suggest that relatively simple feedforward architecture with broad spatial integration is critical to brain-like hierarchy. Our method provides new ways for designing DNNs and understanding the brain in consideration of their representational homology.

scholarly journals Unsupervised seizure localisation with attention-based graph neural networks

2020 ◽  
Author(s):  
Daniele Grattarola ◽  
Lorenzo Livi ◽  
Cesare Alippi ◽  
Richard Wennberg ◽  
Taufik Valiante
Keyword(s):  
Neural Networks ◽  
Brain Activity ◽  
Functional Networks ◽  
Seizure Onset ◽  
The Past ◽  
Seizure Onset Zone ◽  
Brain States ◽  
Graph Neural Networks ◽  
Intracranial Electroencephalography ◽  
The Brain

Abstract Graph neural networks (GNNs) and the attention mechanism are two of the most significant advances in artificial intelligence methods over the past few years. The former are neural networks able to process graph-structured data, while the latter learns to selectively focus on those parts of the input that are more relevant for the task at hand. In this paper, we propose a methodology for seizure localisation which combines the two approaches. Our method is composed of several blocks. First, we represent brain states in a compact way by computing functional networks from intracranial electroencephalography recordings, using metrics to quantify the coupling between the activity of different brain areas. Then, we train a GNN to correctly distinguish between functional networks associated with interictal and ictal phases. The GNN is equipped with an attention-based layer which automatically learns to identify those regions of the brain (associated with individual electrodes) that are most important for a correct classification. The localisation of these regions is fully unsupervised, meaning that it does not use any prior information regarding the seizure onset zone. We report results both for human patients and for simulators of brain activity. We show that the regions of interest identified by the GNN strongly correlate with the localisation of the seizure onset zone reported by electroencephalographers. We also show that our GNN exhibits uncertainty on those patients for which the clinical localisation was also unsuccessful, highlighting the robustness of the proposed approach.

scholarly journals Endogenous and exogenous electric fields as modifiers of brain activity: rational design of noninvasive brain stimulation with transcranial alternating current stimulation

2014 ◽  
Vol 16 (1) ◽  
pp. 93-102 ◽  
Keyword(s):  
Electric Fields ◽  
Brain Stimulation ◽  
Rational Design ◽  
Brain Activity ◽  
Field Potential ◽  
Alternating Current ◽  
Noninvasive Brain Stimulation ◽  
Starting Point ◽  
Recent Developments ◽  
Transcranial Alternating Current Stimulation

Synchronized neuronal activity in the cortex generates weak electric fields that are routinely measured in humans and animal models by electroencephalography and local field potential recordings. Traditionally, these endogenous electric fields have been considered to be an epiphenomenon of brain activity. Recent work has demonstrated that active cortical networks are surprisingly susceptible to weak perturbations of the membrane voltage of a large number of neurons by electric fields. Simultaneously, noninvasive brain stimulation with weak, exogenous electric fields (transcranial current stimulation, TCS) has undergone a renaissance due to the broad scope of its possible applications in modulating brain activity for cognitive enhancement and treatment of brain disorders. This review aims to interface the recent developments in the study of both endogenous and exogenous electric fields, with a particular focus on rhythmic stimulation for the modulation of cortical oscillations. The main goal is to provide a starting point for the use of rational design for the development of novel mechanism-based TCS therapeutics based on transcranial alternating current stimulation, for the treatment of psychiatric illnesses.

scholarly journals Recovery of dynamics and function in spiking neural networks by closed-loop control

2015 ◽  
Author(s):  
Ioannis Vlachos ◽  
Taskin Deniz ◽  
Ad Aertsen ◽  
Arvind Kumar
Keyword(s):  
Neural Networks ◽  
Closed Loop ◽  
Brain Activity ◽  
Open Loop ◽  
Spiking Neural Networks ◽  
Delayed Feedback Control ◽  
Loop Control ◽  
Wide Range ◽  
Underlying Network ◽  
And Function

There is a growing interest in developing novel brain stimulation methods to control disease-related aberrant neural activity and to address basic neuroscience questions. Conventional methods for manipulating brain activity rely on open-loop approaches that usually lead to excessive stimulation and, crucially, do not restore the original computations performed by the network. Thus, they are often accompanied by undesired side-effects. Here, we introduce delayed feedback control (DFC), a conceptually simple but effective method, to control pathological oscillations in spiking neural networks. Using mathematical analysis and numerical simulations we show that DFC can restore a wide range of aberrant network dynamics either by suppressing or enhancing synchronous irregular activity. Importantly, DFC besides steering the system back to a healthy state, it also recovers the computations performed by the underlying network. Finally, using our theory we isolate the role of single neuron and synapse properties in determining the stability of the closed-loop system.

Changes in TMS measures of cortical excitability induced by transcranial direct and alternating current stimulation

2021 ◽  
Author(s):  
Michael A. Nitsche ◽  
Walter Paulus ◽  
Gregor Thut
Keyword(s):  
Brain Stimulation ◽  
Cortical Excitability ◽  
Brain Activity ◽  
Alternating Current ◽  
Transcranial Electrical Stimulation ◽  
Electrical Currents ◽  
After Effects ◽  
Brain Physiology ◽  
Transcranial Alternating Current Stimulation ◽  
Oscillatory Brain Activity

Brain stimulation with weak electrical currents (transcranial electrical stimulation, tES) is known already for about 60 years as a technique to generate modifications of cortical excitability and activity. Originally established in animal models, it was developed as a noninvasive brain stimulation tool about 20 years ago for application in humans. Stimulation with direct currents (transcranial direct current stimulation, tDCS) induces acute cortical excitability alterations, as well as neuroplastic after-effects, whereas stimulation with alternating currents (transcranial alternating current stimulation, tACS) affects primarily oscillatory brain activity but has also been shown to induce neuroplasticity effects. Beyond their respective regional effects, both stimulation techniques have also an impact on cerebral networks. Transcranial magnetic stimulation (TMS) has been pivotal to helping reveal the physiological effects and mechanisms of action of both stimulation techniques for motor cortex application, but also for stimulation of other areas. This chapter will supply the reader with an overview about the effects of tES on human brain physiology, as revealed by TMS.

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