The carcinoembryonic antigen (CEA) gene family belongs to the immunoglobulin supergene family and can be divided into two main subgroups based on sequence comparisons. In humans it is clustered on the long arm of chromosome 19 and consists of approximately 20 genes. The CEA subgroup genes code for CEA and its classical crossreacting antigens, which are mainly membrane‐bound, whereas the other subgroup genes encode the pregnancy‐specific glycoproteins (PSG), which are secreted. Splice variants of individual genes and differential post‐translational modifications of the resulting proteins, e.g., by glycosylation, indicate a high complexity in the number of putative CEA‐related molecules. So far, only a limited number of CEA‐related antigens in humans have been unequivocally assigned to a specific gene. Rodent CEA‐related genes reveal a high sequence divergence and, in part, a completely different domain organization than the human CEA gene family, making it difficult to determine individual gene counterparts. However, rodent CEA‐related genes can be assigned to human subgroups based on similarity of expression patterns, which is characteristic for the subgroups. Various functions have been determined for members of the CEA subgroup in vitro, including cell adhesion, bacterial binding, an accessory role for collagen binding or ecto‐ATPases activity. Based on all that is known so far on its biology, the clinical outlook for the CEA family has been reassessed.

Background: Deep brain stimulation (DBS) for the treatment of movement disorders has provided researchers with an opportunity to record electrical oscillatory activity from electrodes implanted in deep brain structures. Extracellular activity recorded from a population of neurons, termed local field potentials (LFPs), has shed light on the pathophysiology of movement disorders and holds the potential to lead to refinement in existing treatments. Objective: This paper reviews the clinical significance of LFPs recorded from macroelectrodes implanted in basal ganglia and thalamic targets for the treatment of Parkinson's disease, essential tremor and dystonia. Methods: Neural population dynamics and subthreshold events, which are undetectable by single-unit recordings, can be examined with frequency band analysis of LFPs (frequency range: 1-250 Hz). Results: Of clinical relevance, reliable correlations between motor symptoms and components of the LFP power spectrum suggest that LFPs may serve as a biomarker for movement disorders. In particular, Parkinson's rigidity has been shown to correlate with the power of beta oscillations (13-30 Hz), and essential tremor coheres with oscillations of 8-27 Hz. Furthermore, evidence indicates that the optimal contacts for DBS programming can be predicted from the anatomic location of beta and gamma bands (48-200 Hz). Conclusion: LFP analysis has implications for improved electrode targeting and the development of a real-time, individualized, ‘closed-loop' stimulation system.

In the treatment of brain tumors, surgical intervention remains a common and effective therapeutic option. Recent advances in neuroimaging have provided neurosurgeons with new tools to overcome the challenge of differentiating healthy tissue from tumor-infiltrated tissue, with the aim of increasing the likelihood of maximizing the extent of resection volume while minimizing injury to functionally important regions. Novel applications of diffusion tensor imaging (DTI), and DTI-derived tractography (DDT) have demonstrated that preoperative, non-invasive mapping of eloquent cortical regions and functionally relevant white matter tracts (WMT) is critical during surgical planning to reduce postoperative deficits, which can decrease quality of life and overall survival. In this review, we summarize the latest developments of applying DTI and tractography in the context of resective surgery and highlight its utility within each stage of the neurosurgical workflow: preoperative planning and intraoperative management to improve postoperative outcomes.

Computational competitions are the standard for benchmarking medical image analysis algorithms, but they typically use small curated test datasets acquired at a few centers, leaving a gap to the reality of diverse multicentric patient data. To this end, the Federated Tumor Segmentation (FeTS) Challenge represents the paradigm for real-world algorithmic performance evaluation. The FeTS challenge is a competition to benchmark (i) federated learning aggregation algorithms and (ii) state-of-the-art segmentation algorithms, across multiple international sites. Weight aggregation and client selection techniques were compared using a multicentric brain tumor dataset in realistic federated learning simulations, yielding benefits for adaptive weight aggregation, and efficiency gains through client sampling. Quantitative performance evaluation of state-of-the-art segmentation algorithms on data distributed internationally across 32 institutions yielded good generalization on average, albeit the worst-case performance revealed data-specific modes of failure. Similar multi-site setups can help validate the real-world utility of healthcare AI algorithms in the future.

Sleep is a fundamental homeostatic process, and disorders of sleep can greatly affect quality of life. Parkinson’s disease (PD) is highly comorbid for a spectrum of sleep disorders and deep brain stimulation (DBS) of the subthalamic nucleus (STN) has been reported to improve sleep architecture in PD. We studied local field potential (LFP) recordings in PD subjects undergoing STN-DBS over the course of a full-night’s sleep. We examined the changes in oscillatory activity recorded from STN between ultradian sleep states to determine whether sleep-stage dependent spectral patterns might reflect underlying dysfunction. For this study, PD (n=10) subjects were assessed with concurrent polysomnography and LFP recordings from the DBS electrodes, for an average of 7.5 hours in ‘off’ dopaminergic medication state. Across subjects, we found conserved spectral patterns among the canonical frequency bands (delta 0–3 Hz, theta 3–7 Hz, alpha 7–13 Hz, beta 13–30 Hz, gamma 30–90 Hz and high frequency 90–350 Hz) that were associated with specific sleep cycles: delta (0–3 Hz) activity during non-rapid eye movement (NREM) associated stages was greater than during Awake, whereas beta (13–30 Hz) activity during NREM states was lower than Awake and rapid eye movement (REM). In addition, all frequency bands were significantly different between NREM states and REM. However, each individual subject exhibited a unique mosaic of spectral interrelationships between frequency bands. Our work suggests that LFP recordings from human STN differentiate between sleep cycle states, and sleep-state specific spectral mosaics may provide insight into mechanisms underlying sleep pathophysiology.

Neurons in the medial nucleus of the trapezoid body (MNTB) receive prominent excitatory input through the calyx of Held, a giant synapse that produces large and fast excitatory currents. MNTB neurons also receive inhibitory glycinergic inputs that are also large and fast, and match the calyceal excitation in terms of synaptic strength. GABAergic inputs provide additional inhibition to MNTB neurons. Inhibitory inputs to MNTB modify spiking of MNTB neurons both in-vitro and in-vivo, underscoring their importance. Surprisingly, the origin of the inhibitory inputs to MNTB has not been shown conclusively. We performed retrograde tracing, anterograde tracing, immunohistochemical experiments, and electrophysiological recordings to address this question. The results support the ventral nucleus of the trapezoid body (VNTB) as at least one major source of glycinergic input to MNTB. VNTB fibers enter the ipsilateral MNTB, travel along MNTB principal neurons and produce several bouton-like presynaptic terminals. Further, the GABAergic component of the inhibitory input undergoes a developmental decrease in amplitude that is matched in time by a reduction in expression of a GABA synthetic enzyme in VNTB principal neurons.