This editorial, inspired by a recent study published in the World Journal of Gastrointestinal Oncology, covers the research findings on microbiota changes in various diseases. In recurrent colorectal polyps, the abundances of Klebsiella, Parvimonas, and Clostridium increase, while those of Bifidobacterium and Lactobacillus decrease. This dysbiosis may promote the formation and recurrence of polyps. Similar microbial changes have also been observed in colorectal cancer, inflammatory bowel disease, autism spectrum disorder, and metabolic syndrome, indicating the role of increased pathogens and decreased probiotics in these conditions. Regulating the gut microbiota, particularly by increasing probiotic levels, may help prevent polyp recurrence and promote gut health. This microbial intervention strategy holds promise as an adjunctive treatment for patients with colorectal polyps.

Despite promising advancements, closed-loop neurostimulation for drug-resistant epilepsy (DRE) still relies on manual tuning and produces variable outcomes, while automated predictable algorithms remain an aspiration. As a fundamental step towards addressing this gap, here we study predictive dynamical models of human intracranial EEG (iEEG) response under parametrically rich neurostimulation. Using data from n = 13 DRE patients, we find that stimulation-triggered switched-linear models with ~300 ms of causal historical dependence best explain evoked iEEG dynamics. These models are highly consistent across different stimulation amplitudes and frequencies, allowing for learning a generalizable model from abundant STIM OFF and limited STIM ON data. Further, evoked iEEG in nearly all subjects exhibited a distance-dependent pattern, whereby stimulation directly impacts the actuation site and nearby regions (≲ 20 mm), affects medium-distance regions (20 ~ 100 mm) through network interactions, and hardly reaches more distal areas (≳ 100 mm). Peak network interaction occurs at 60 ~ 80 mm from the stimulation site. Due to their predictive accuracy and mechanistic interpretability, these models hold significant potential for model-based seizure forecasting and closed-loop neurostimulation design.

The open-loop nature of conventional deep brain stimulation (DBS) produces continuous and excessive stimulation to patients which contributes largely to increased prevalence of adverse side effects. Cerebellar ataxia is characterized by abnormal Purkinje cells (PCs) dendritic arborization, loss of PCs and motor coordination, and muscle weakness with no effective treatment. We aim to develop a real-time field-programmable gate array (FPGA) prototype targeting the deep cerebellar nuclei (DCN) to close the loop for ataxia using conditional double knockout mice with deletion of PC-specific LIM homeobox (Lhx)1 and Lhx5, resulting in abnormal dendritic arborization and motor deficits.

We implanted multielectrode array in the DCN and muscles of ataxia mice. The beneficial effect of open-loop DCN-DBS or closed-loop DCN-DBS was compared by motor behavioral assessments, electromyography (EMG), and neural activities (neurospike and electroencephalogram) in freely moving mice. FPGA board, which performed complex real-time computation, was used for closed-loop DCN-DBS system.

Closed-loop DCN-DBS was triggered only when symptomatic muscle EMG was detected in a real-time manner, which restored motor activities, electroencephalogram activities and neurospike properties completely in ataxia mice. Closed-loop DCN-DBS was more effective than an open-loop paradigm as it reduced the frequency of DBS.

Our real-time FPGA-based DCN-DBS system could be a potential clinical strategy for alleviating cerebellar ataxia and other movement disorders.

Parkinson's disease (PD) is the second most common neurodegenerative disease bearing a severe social and economic impact. So far, there is no known disease modifying therapy and the current available treatments are symptom oriented. Deep Brain Stimulation (DBS) is established as an effective treatment for PD, however current systems lag behind today's technological potential. Adaptive DBS, where stimulation parameters depend on the patient's physiological state, emerges as an important step towards "smart" DBS, a strategy that enables adaptive stimulation and personalized therapy. This new strategy is facilitated by currently available neurotechnologies allowing the simultaneous monitoring of multiple signals, providing relevant physiological information. Advanced computational models and analytical methods are an important tool to explore the richness of the available data and identify signal properties to close the loop in DBS. To tackle this challenge, machine learning (ML) methods applied to DBS have gained popularity due to their ability to make good predictions in the presence of multiple variables and subtle patterns. ML based approaches are being explored at different fronts such as the identification of electrophysiological biomarkers and the development of personalized control systems, leading to effective symptom relief. In this review, we explore how ML can help overcome the challenges in the development of closed-loop DBS, particularly its role in the search for effective electrophysiology biomarkers. Promising results demonstrate ML potential for supporting a new generation of adaptive DBS, with better management of stimulation delivery, resulting in more efficient and patient-tailored treatments.

Closed-loop deep brain stimulation (DBS) can apply on-demand stimulation based on the feedback signal (e.g. beta band oscillation), which is deemed to lower side effects of clinically used open-loop DBS. To facilitate the application of model-based closed-loop DBS in clinical, studies must consider state variations, e.g., variation of desired signal with different movement conditions and variation of model parameters with time. This paper proposes to use the controlled autoregressive (CAR)-fuzzy control algorithm to modulate the pathological beta band (13-35 Hz) oscillation of a basal ganglia-cortex-thalamus model. The CAR model is used to identify the relationship between DBS frequency parameter and beta oscillation power. Then the error between the one-step-ahead predicted beta power of CAR model and the desired value is innovatively used as the input of fuzzy controller to calculate the next step stimulation frequency. Compared with 130 Hz open-loop DBS, the proposed closed-loop DBS method could lower the mean stimulation frequency to 74.04 Hz with similar beta oscillation suppression performance. The Mamdani fuzzy controller is selected because which could establish fuzzy controller rules according to human operation experience. Adding prediction module to closed-loop control improves the accuracy of fuzzy control, compared with proportional-integral control and fuzzy control, the proposed CAR-fuzzy control algorithm has higher tracking reliability, response speed and robustness.

Deep brain stimulation (DBS) is a well-established technique for the treatment of movement and psychiatric disorders through the modulation of neural oscillatory activity and synaptic plasticity. The central thalamus (CT) has been indicated as a potential target for stimulation to enhance memory. However, the mechanisms underlying local field potential (LFP) oscillations and memory enhancement by CT-DBS remain unknown. In this study, we used CT-DBS to investigate the mechanisms underlying the changes in oscillatory communication between the CT and hippocampus, both of which are involved in spatial working memory. Local field potentials (LFPs) were recorded from microelectrode array implanted in the CT, dentate gyrus, cornu ammonis (CA) region 1, and CA region 3. Functional connectivity (FC) strength was assessed by LFP-LFP coherence calculations for these brain regions. In addition, a T-maze behavioral task using a rat model was performed to assess the performance of spatial working memory. In DBS group, our results revealed that theta oscillations significantly increased in the CT and hippocampus compared with that in sham controls. As indicated by coherence, the FC between the CT and hippocampus significantly increased in the theta band after CT-DBS. Moreover, Western blotting showed that the protein expressions of the dopamine D1 and α4-nicotinic acetylcholine receptors were enhanced, whereas that of the dopamine D2 receptor decreased in the DBS group. In conclusion, the use of CT-DBS resulted in elevated theta oscillations, increased FC between the CT and hippocampus, and altered synaptic plasticity in the hippocampus, suggesting that CT-DBS is an effective approach for improving spatial working memory.

Social deficit is a core symptom in autism spectrum disorder (ASD). Although deep brain stimulation (DBS) has been proposed as a potential treatment for ASD, an ideal target nucleus is yet to be identified. DBS at the central thalamic nucleus (CTN) is known to alter corticostriatal and limbic circuits, and subsequently increase the exploratory motor behaviors, cognitive performance, and skill learning in neuropsychiatric and neurodegenerative disorders.

We first investigated the ability of CTN-DBS to selectively engage distinct brain circuits and compared the spatial distribution of evoked network activity and modulation. Second, we investigated whether CTN-DBS intervention improves social interaction in a valproic acid-exposed ASD rat offspring model.

Brain regions activated through CTN-DBS by using a magnetic resonance (MR)-compatible neural probe, which is capable of inducing site-selective microstimulations during functional MRI (fMRI), were investigated. We then performed functional connectivity MRI, the three-chamber social interaction test, and Western blotting analyses to evaluate the therapeutic efficacy of CTN-DBS in an ASD rat offspring model.

The DBS-evoked fMRI results indicated that the activated brain regions were mainly located in cortical areas, limbic-related areas, and the dorsal striatum. We observed restoration of brain functional connectivity (FC) in corticostriatal and corticolimbic circuits after CTN-DBS, accompanied with increased social interaction and decreased social avoidance in the three-chamber social interaction test. The dopamine D2 receptor decreased significantly after CTN-DBS treatment, suggesting changes in synaptic plasticity and alterations in the brain circuits.

Applying CTN-DBS to ASD rat offspring increased FC and altered the synaptic plasticity in the corticolimbic and the corticostriatal circuits. This suggests that CTN-DBS could be an effective treatment for improving the social behaviors of individuals with ASD.