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Transcranial magnetic stimulation (TMS) represents a distinctive technique for non-invasive brain stimulation. Recent advancements in image processing have enabled the enhancement of TMS by integrating magnetic resonance imaging (MRI) modalities with TMS via a neuronavigation system. The aim of this study is to assess the efficacy of navigated TMS for cortical mapping in comparison to surgical mapping using direct electrical stimulation (DES). This study involved 30 neurosurgical procedures for tumors located in or adjacent to the precentral gyrus. The DES points were compared with TMS responses based on the original distances of vectorial modules. There was a notable similarity in the points obtained from the two mapping methods. The distances between the geometric centers of TMS and DCS were 4.85 ± 1.89 mm. A strong correlation was identified between these vectorial points (r = 0.901, p < 0.001). The motor threshold in TMS was highest in the motor cortex adjacent to the tumor compared to the normal cortex (p < 0.001). Patients with deficits exhibited excellent accuracy in both methods. In view of this, TMS demonstrated reliable and precise application in brain mapping, which is a promising method for preoperative functional mapping in motor cortex tumor surgery.

Deep-brain stimulation (DBS) is a highly effective and safe medical treatment that improves the lives of patients with a wide range of neurological and psychiatric diseases. It has been established as a first-line tool in the treatment of these conditions for the past two decades. Closed-loop deep-brain stimulation (CLDBS) advances this tool further by automatically adjusting the stimulation parameters in real time based on the brain’s response. In this context, this paper presents a low-noise amplifier (LNA) and a neurostimulator circuit fabricated using the low-power/low-voltage 65 nm CMOS process from TSMC. The circuits are specifically designed for implantable applications. To achieve the best tradeoff between input-referred noise and power consumption, metaheuristic algorithms were employed to determine and optimize the dimensions of the LNA devices during the design phase. Measurement results showed that the LNA had a gain of 41.2 dB; a 3 dB bandwidth spanning over three decades, from 1.5 Hz to 11.5 kHz; a power consumption of 5.9 µW; and an input-referred noise of 3.45 µVRMS, from 200 Hz to 11.5 kHz. The neurostimulator circuit is a programmable Howland current pump. Measurements have shown its capability to generate currents with arbitrary shapes and ranging from −325 µA to +318 µA. Simulations indicated a quiescent power consumption of 0.13 µW, with zero neurostimulation current. Both the LNA and the neurostimulator circuits are supplied with a 1.2 V voltage and occupy a microdevice area of 145 µm × 311 µm and 88 µm × 89 µm, respectively, making them suitable for implantation in applications involving closed-loop deep-brain stimulation.

Deep brain stimulation targeting the posterior hypothalamus (pHyp-DBS) is being investigated as a treatment for refractory aggressive behavior, but its mechanisms of action remain elusive. We conducted an integrated imaging analysis of a large multi-centre dataset, incorporating volume of activated tissue modeling, probabilistic mapping, normative connectomics, and atlas-derived transcriptomics. Ninety-one percent of the patients responded positively to treatment, with a more striking improvement recorded in the pediatric population. Probabilistic mapping revealed an optimized surgical target within the posterior-inferior-lateral region of the posterior hypothalamic area. Normative connectomic analyses identified fiber tracts and functionally connected with brain areas associated with sensorimotor function, emotional regulation, and monoamine production. Functional connectivity between the target, periaqueductal gray and key limbic areas – together with patient age – were highly predictive of treatment outcome. Transcriptomic analysis showed that genes involved in mechanisms of aggressive behavior, neuronal communication, plasticity and neuroinflammation might underlie this functional network.