A 16-Channel Low-Power Neural Connectivity Extraction and Phase-Locked Deep Brain Stimulation SoC

Neural Decoding and Feature Selection Techniques for Closed-Loop Control of Defensive Behavior

Objective

Many psychiatric disorders involve excessive avoidant or defensive behavior, such as avoidance in anxiety and trauma disorders or defensive rituals in obsessive-compulsive disorders. Developing algorithms to predict these behaviors from local field potentials (LFPs) could serve as foundational technology for closed-loop control of such disorders. A significant challenge is identifying the LFP features that encode these defensive behaviors.

Approach

We analyzed LFP signals from the infralimbic cortex and basolateral amygdala of rats undergoing tone-shock conditioning and extinction, standard for investigating defensive behaviors. We utilized a comprehensive set of neuro-markers across spectral, temporal, and connectivity domains, employing SHapley Additive exPlanations for feature importance evaluation within Light Gradient-Boosting Machine models. Our goal was to decode three commonly studied avoidance/defensive behaviors: freezing, bar-press suppression, and motion (accelerometry), examining the impact of different features on decoding performance.

Main results

Band power and band power ratio between channels emerged as optimal features across sessions. High-gamma (80-150 Hz) power, power ratios, and inter-regional correlations were more informative than other bands that are more classically linked to defensive behaviors. Focusing on highly informative features enhanced performance. Across 4 recording sessions with 16 subjects, we achieved an average coefficient of determination of 0.5357 and 0.3476, and Pearson correlation coefficients of 0.7579 and 0.6092 for accelerometry jerk and bar press rate, respectively. Utilizing only the most informative features revealed differential encoding between accelerometry and bar press rate, with the former primarily through local spectral power and the latter via inter-regional connectivity. Our methodology demonstrated remarkably low time complexity, requiring <110 ms for training and <1 ms for inference.

Significance

Our results demonstrate the feasibility of accurately decoding defensive behaviors with minimal latency, using LFP features from neural circuits strongly linked to these behaviors. This methodology holds promise for real-time decoding to identify physiological targets in closed-loop psychiatric neuromodulation.

Brain Feature Extraction With an Artifact-Tolerant Multiplexed Time-Encoding Neural Frontend for True Real-Time Closed-Loop Neuromodulation

Closed-loop neuromodulation is emerging as a more effective and targeted solution for the treatment of neurological symptoms compared to traditional open-loop stimulation. The majority of the present designs lack the ability to continuously record brain activity during electrical stimulation; hence they cannot fully monitor the treatment's effectiveness. This is due to the large stimulation artifacts that can saturate the sensitive readout circuits. To overcome this challenge, this work presents a rapid-artifact-recovery time-multiplexed neural readout frontend in combination with backend linear interpolation to reconstruct the artifact-corrupted local field potentials' (LFP) features. Our hybrid technique is an alternative approach to avoid power-hungry large-dynamic-range readout architectures or large and complex artifact template subtraction circuits. We discuss the design and measurements of a prototype implementation of the proposed readout frontend in 180-nm CMOS. It combines time multiplexing and time-domain conversion in a novel 13-bit incremental ADC, requiring only 0.0018 mm2/channel of readout area despite the large 180-nm CMOS process used, while consuming only 4.51 μW/channel. This is the smallest reported area for stimulation-voltage-compatible technologies (i.e. ≥ 65 nm). The frontend also yields a best-in-class peak total harmonic distortion of -72.6 dB @2.5-mVpp input, thanks to its implicit DAC mismatch-error shaping property. We employ the chip to measure brain LFP signals corrupted with artifacts, then perform linear interpolation and feature extraction on the measured signals and evaluate the reconstruction quality, using a set of sixteen commonly used features and three stimulation scenarios. The results show relative accuracies above 95% with respect to the situation without artifacts. This work is an ideal candidate for integration in high-channel-count true closed-loop neuromodulation systems.

A LFP/AP Mode Reconfigurable Analog Front-End Combining an Electrical EEEG-iEEG Model for the Closed-Loop VNS

This article presents a local field potential (LFP)/action potential (AP) mode reconfigurable analog front-end (AFE) dedicated for the closed-loop vagus nerve stimulator (VNS). It combines an inverse electrical model of the intracranial electroencephalogram (iEEG) conducting in the brain tissues and been recorded at scalp as the extended electroencephalogram (EEEG). The AFE contains a LFP/AP mode reconfigurable EEEG preamplifier, a tunable integrator to compensate the effect of either the recording electrodes or head tissues, and an adder. The LFP/AP mode reconfigurable EEEG preamplifier consists of a tunable chopper-stabilized amplifier (CSA) and a 2nd-order tunable low pass filter (LPF). For better separation of LFP and AP signals, a high-order DC servo loop (DSL) characterized as a 2nd-order DSL in parallel with a 1st-order DSL is exploited in the tunable CSA to achieve a tunable high-pass frequency with a stopband attenuation slope (SAS) of +40 dB/dec. In addition, the tunable LPF can obtain a tunable low-pass frequency with a SAS of -40 dB/dec and provide additional 20 dB gain for AP signals. Fabricated in a SMIC 180 nm CMOS technology, and in the LFP band (0.5 Hz-200 Hz) and AP band (300 Hz-5 kHz), the measured mid-band gains of the LFP/AP mode reconfigurable EEEG preamplifier are 39.6 dB and 59.5 dB, the input-referred noises (IRNs) are 2.2 μVrms and 6.3 μVrms, the DC/in-band input impedances are 1.27/1.26 GΩ and 0.3/0.22 GΩ, respectively. The power consumption per channel AFE is 6.3 μW, and the die area is 1.4 mm × 0.25 mm.

From basic sciences and engineering to epileptology: A translational approach

Collaborative efforts between basic scientists, engineers, and clinicians are enabling translational epileptology. In this article, we summarize the recent advances presented at the International Conference for Technology and Analysis of Seizures (ICTALS 2022): (1) novel developments of structural magnetic resonance imaging; (2) latest electroencephalography signal-processing applications; (3) big data for the development of clinical tools; (4) the emerging field of hyperdimensional computing; (5) the new generation of artificial intelligence (AI)-enabled neuroprostheses; and (6) the use of collaborative platforms to facilitate epilepsy research translation. We highlight the promise of AI reported in recent investigations and the need for multicenter data-sharing initiatives.