Closed-loop neurostimulation for the treatment of psychiatric disorders

Sellers, Kristin K.; Cohen, Joshua L.; Khambhati, Ankit N.; Fan, Joline M.; Lee, A. Moses; Chang, Edward F.; Krystal, Andrew D.

doi:10.1038/s41386-023-01631-2

Cited by 6 publications

(3 citation statements)

References 377 publications

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“…Along several decades of development, neurostimulation has benefited extensively from scientific progress and technological breakthroughs, such as a better understanding of the neurophysical basis of the interaction between electromagnetic fields and brain tissue ( Nunez and Harth, 2005 ; Buzsáki and Vöröslakos, 2023 ), paradigm-shifting neuroscientific discoveries related to the processing of neural information ( Varela et al, 2001 ; Buzsáki and Watson, 2012 ), innovative neural interfaces ( Panuccio et al, 2018 ), and powerful signal processing methods, including the usage of artificial intelligence/machine learning tools ( Fellous et al, 2019 ; Chandrabhatla et al, 2023 ), and neuromorphic strategies ( Chiappalone et al, 2022 ; Christensen et al, 2022 ). By its turn, these allowed for the exploration of a series of novel stimulation paradigms, including temporally spatial complex stimulus patterns ( Cota et al, 2023 ), and closed-loop modes of operation ( Panuccio et al, 2016 ; Iturrate et al, 2018 ; Sellers et al, 2024 ). Collectively, these advancements are spurring a new era of disruptive neurostimulation, referred to as electroceuticals ( Famm et al, 2013 ; Reardon, 2014 , 2017 ), which can target specific nerves or neural pathways, addressing various chronic diseases and conditions, not limited to neuronal disorders.…”

Section: Introductionmentioning

confidence: 99%

Personalized strategies of neurostimulation: from static biomarkers to dynamic closed-loop assessment of neural function

Carè,

Chiappalone,

Cota

2024

Front. Neurosci.

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Despite considerable advancement of first choice treatment (pharmacological, physical therapy, etc.) over many decades, neurological disorders still represent a major portion of the worldwide disease burden. Particularly concerning, the trend is that this scenario will worsen given an ever expanding and aging population. The many different methods of brain stimulation (electrical, magnetic, etc.) are, on the other hand, one of the most promising alternatives to mitigate the suffering of patients and families when conventional treatment fall short of delivering efficacious treatment. With applications in virtually all neurological conditions, neurostimulation has seen considerable success in providing relief of symptoms. On the other hand, a large variability of therapeutic outcomes has also been observed, particularly in the usage of non-invasive brain stimulation (NIBS) modalities. Borrowing inspiration and concepts from its pharmacological counterpart and empowered by unprecedented neurotechnological advancement, the neurostimulation field has seen in recent years a widespread of methods aimed at the personalization of its parameters, based on biomarkers of the individuals being treated. The rationale is that, by taking into account important factors influencing the outcome, personalized stimulation can yield a much-improved therapy. Here, we review the literature to delineate the state-of-the-art of personalized stimulation, while also considering the important aspects of the type of informing parameter (anatomy, function, hybrid), invasiveness, and level of development (pre-clinical experimentation versus clinical trials). Moreover, by reviewing relevant literature on closed loop neuroengineering solutions in general and on activity dependent stimulation method in particular, we put forward the idea that improved personalization may be achieved when the method is able to track in real time brain dynamics and adjust its stimulation parameters accordingly. We conclude that such approaches have great potential of promoting the recovery of lost functions and enhance the quality of life for patients.

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Section: Introductionmentioning

confidence: 99%

Personalized strategies of neurostimulation: from static biomarkers to dynamic closed-loop assessment of neural function

Carè,

Chiappalone,

Cota

2024

Front. Neurosci.

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“…Moreover, high-frequency rTMS of the DLPFC has been reported to positively impact memory and executive function in BD patients 20,21 . However, the clinical efficacy of rTMS for cognitive impairments in BD was inconsistent [22][23][24] , possibly due to variability in target selection and accuracy of localization 25,26 . Traditional rTMS often failed to consider structural and functional differences between individuals, resulting in suboptimal stimulation outcomes and sometimes missing the abnormal target regions 27 .…”

Section: Introductionmentioning

confidence: 99%

Cognitive Enhancement in Bipolar Disorder: A Double-Blind, Randomized Controlled Trial Utilizing a Novel DTI-Guided Multimodal Neuro-stimulation Protocol

Wang,

Zhang,

Zhou

et al. 2024

Preprint

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Background: Traditional neuromodulation strategies for enhancing cognitive abilities in bipolar disorder (BD) patients have shown promise, yet there remains a need for novel intervention modalities to improve therapeutic outcomes. Methods: This study introduces a novel multi-modal neuro-stimulaton (MNS) protocol using individualized DTI data to identify fiber tracts between the DLPFC and dACC. The highest structural connectivity point is selected as the individualized stimulation target, which is targeted using a combination of optimized tACS and robot-assisted navigated rTMS. A double-blind randomized controlled trial (Trial registration number:NCT05964777) was conducted to investigate the clinical efficacy of this innovative neuromodulation approach on cognitive abilities in BD patients. One hundred BD patients were randomly assigned to four groups: Group A (Active tACS-Active rTMS (MNS Protocol)), Group B (Sham tACS-Active rTMS), Group C (Active tACS-Sham rTMS ), and Group D (Sham tACS-Sham rTMS). Participants underwent 15 sessions over three weeks. Cognitive assessments (THINC integrated tool) were conducted at baseline (Week 0), post-treatment (Week 3), and follow-up (Week 8). Results: Sixty-six participants completed all 15 sessions. Group A (MNS Protocol) showed superior improvements in Spotter CRT, TMT, and DSST scores compared to other groups at Week 3, with sustained cognitive enhancement in Spotter CRT at Week 8 (P < 0.01). Only Group A exhibited significant activation in the left frontal region post-MNS intervention. The novel MNS protocol was well tolerated, with no significant side effects observed. Conclusions: DTI-guided multimodal neuro-stimulation mode significantly improves cognitive impairments and is safe for BD patients.

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“…Real-time neural signal processing is pivotal for brain-machine interfaces (BMI) 1,2 , neurofeedback therapies 3 , and closed-loop neural perturbations 4,5 . These applications rely on rapidly recognizing patterns in neural signals to trigger predefined feedback—either controlling external devices or modulating brain activity.…”

Section: Introductionmentioning

confidence: 99%

Low-latency extracellular spike assignment for high-density electrodes at single-neuron resolution

Lai,

Kim,

Lustig

et al. 2023

Preprint

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Real-time neural signal processing is essential for brain-machine interfaces and closed-loop neuronal perturbations. However, most existing applications sacrifice cell-specific identity and temporal spiking information for speed. We developed a hybrid hardware-software system that utilizes a Field Programmable Gate Array (FPGA) chip to acquire and process data in parallel, enabling individual spikes from many simultaneously recorded neurons to be assigned single-neuron identities with 1-millisecond latency. The FPGA assigns labels, validated with ground-truth data, by comparing multichannel spike waveforms from tetrode or silicon probe recordings to a spike-sorted model generated offline in software. This platform allowed us to rapidly inactivate a region in vivo based on spikes from an upstream neuron before these spikes could excite the downstream region. Furthermore, we could decode animal location within 3 ms using data from a population of individual hippocampal neurons. These results demonstrate our system's suitability for a broad spectrum of research and clinical applications.

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Closed-loop neurostimulation for the treatment of psychiatric disorders

Cited by 6 publications

References 377 publications

Personalized strategies of neurostimulation: from static biomarkers to dynamic closed-loop assessment of neural function

Personalized strategies of neurostimulation: from static biomarkers to dynamic closed-loop assessment of neural function

Cognitive Enhancement in Bipolar Disorder: A Double-Blind, Randomized Controlled Trial Utilizing a Novel DTI-Guided Multimodal Neuro-stimulation Protocol

Low-latency extracellular spike assignment for high-density electrodes at single-neuron resolution

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