Structural network heterogeneities and network dynamics: a possible dynamical mechanism for hippocampal memory reactivation

The hippocampus has the capacity for reactivating recently acquired memories [1-3] and it is hypothesized that one of the functions of sleep reactivation is the facilitation of consolidation of novel memory traces [4-11]. The dynamic and network processes underlying such a reactivation remain, however, unknown. We show that such a reactivation characterized by local, self-sustained activity of a network region may be an inherent property of the recurrent excitatory-inhibitory network with a heterogeneous structure. The entry into the reactivation phase is mediated through a physiologically feasible regulation of global excitability and external input sources, while the reactivated component of the network is formed through induced network heterogeneities during learning. We show that structural changes needed for robust reactivation of a given network region are well within known physiological parameters [12,13].Comment: 16 pages, 5 figure

Using Low-Dose Radiation to Potentiate the Effect of Induction Chemotherapy in Head and Neck Cancer: Results of a Prospective Phase 2 Trial

Purpose: Low-dose fractionated radiation therapy (LDFRT) induces effective cell killing through hyperradiation sensitivity and potentiates effects of chemotherapy. We report our second investigation of LDFRT as a potentiator of the chemotherapeutic effect of induction carboplatin and paclitaxel in locally advanced squamous cell cancer of the head and neck (SCCHN). Experimental Design: Two cycles of induction therapy were given every 21 days: paclitaxel (75 mg/m2) on days 1, 8, and 15; carboplatin (area under the curve 6) day 1; and LDFRT 50 cGy fractions (2 each on days 1, 2, 8, and 15). Objectives included primary site complete response rate; secondary included overall survival, progression-free survival (PFS), disease-specific survival, and toxicity. Results: A total of 24 evaluable patients were enrolled. Primary sites included oropharynx (62.5%), larynx (20.8%), oral cavity (8.3%), and hypopharynx (8.3%). Grade 3/4 toxicities included neutropenia (20%), leukopenia (32%), dehydration/hypotension (8%), anemia (4%), infection (4%), pulmonary/allergic rhinitis (4%), and diarrhea (4%). Primary site response rate was 23/24 (95.8%): 15/24 (62.5%) complete response, 8/24 (33.3%) partial response, and 1/24 (4.2%) stable disease. With median follow-up of 7.75 years, 9-year rates for overall survival were 49.4% (95% confidence interval [CI], 30.5-79.9), PFS was 72.2% (CI, 55.3-94.3), and disease-specific survival was 65.4% (44.3-96.4). Conclusion: Chemopotentiating LDFRT combined with paclitaxel and carboplatin is effective in SCCHN and provided an excellent median overall survival of 107.2 months, with median PFS not yet reached in this locally advanced SCCHN cohort. This compares favorably to prior investigations and caused fewer grade 3 and 4 toxicities than more intensive, 3-drug induction regimens. This trial demonstrates the innovative use of LDFRT as a potentiator of chemotherapy

Radiation‐Induced Oral Mucositis Hamster Model Using a Linear Accelerator Enhances Clinical Relevance of Preclinical Studies for Treatment Strategy Investigation

Translational animal models for oral mucositis (OM) are necessary to simulate and assess the bioclinical effects and response in humans. These models should simulate high levels of radiation exposure that leads to oxidative stress and inflammatory‐initiated tissue changes. Hamster models have been extensively studied to observe pathological effects of radiation exposure and help in the development of effective treatments. To successfully evaluate the potential for treatment regimens with consistency and relevance, a radiation‐induced OM hamster model was developed using a clinical linear accelerator utilized by cancer patients daily. The dose exposure to the isolated, everted cheek pouch of a hamster, as well as the progression of injury, pro‐inflammatory marker, histological, and elasticity analyses of the buccal pouch were conducted to verify replicability and reproducibility of the injury model. The findings from this model demonstrated its ability to consistently induce injury and resolution over 28 days using an acute dose of 60 Gy. This model was developed to enhance clinical relevance when evaluating potential efficacious treatments and can now be utilized in efficacy studies to better evaluate developed therapeutics in a preclinical model that is easy to translate to clinical studies

Chloroquine-Inducible Par-4 Secretion Is Essential for Tumor Cell Apoptosis and Inhibition of Metastasis

The induction of tumor suppressor proteins capable of cancer cell apoptosis represents an attractive option for the re-purposing of existing drugs. We report that the anti-malarial drug, chloroquine (CQ), is a robust inducer of Par-4 secretion from normal cells in mice and cancer patients in a clinical trial. CQ-inducible Par-4 secretion triggers paracrine apoptosis of cancer cells and also inhibits metastatic tumor growth. CQ induces Par-4 secretion via the classical secretory pathway that requires the activation of p53. Mechanistically, p53 directly induces Rab8b, a GTPase essential for vesicle transport of Par-4 to the plasma membrane prior to secretion. Our findings indicate that CQ induces p53- and Rab8b-dependent Par-4 secretion from normal cells for Par-4-dependent inhibition of metastatic tumor growth

Online Continual Learning on Sequences

Online continual learning (OCL) refers to the ability of a system to learn over time from a continuous stream of data without having to revisit previously encountered training samples. Learning continually in a single data pass is crucial for agents and robots operating in changing environments and required to acquire, fine-tune, and transfer increasingly complex representations from non-i.i.d. input distributions. Machine learning models that address OCL must alleviate \textit{catastrophic forgetting} in which hidden representations are disrupted or completely overwritten when learning from streams of novel input. In this chapter, we summarize and discuss recent deep learning models that address OCL on sequential input through the use (and combination) of synaptic regularization, structural plasticity, and experience replay. Different implementations of replay have been proposed that alleviate catastrophic forgetting in connectionists architectures via the re-occurrence of (latent representations of) input sequences and that functionally resemble mechanisms of hippocampal replay in the mammalian brain. Empirical evidence shows that architectures endowed with experience replay typically outperform architectures without in (online) incremental learning tasks.Comment: L. Oneto et al. (eds.), Recent Trends in Learning From Data, Studies in Computational Intelligence 89

Development and Validation of Nomograms Predictive of Overall and Progression-Free Survival in Patients With Oropharyngeal Cancer

Purpose Treatment of oropharyngeal squamous cell carcinoma (OPSCC) is evolving toward risk-based modification of therapeutic intensity, which requires patient-specific estimates of overall survival (OS) and progression-free survival (PFS). Methods To develop and validate nomograms for OS and PFS, we used a derivation cohort of 493 patients with OPSCC with known p16 tumor status (surrogate of human papillomavirus) and cigarette smoking history (pack-years) randomly assigned to clinical trials using platinum-based chemoradiotherapy (NRG Oncology Radiation Therapy Oncology Group [RTOG] 0129 and 0522). Nomograms were created from Cox models and internally validated by use of bootstrap and cross-validation. Model discrimination was measured by calibration plots and the concordance index. Nomograms were externally validated in a cohort of 153 patients with OPSCC randomly assigned to a third trial, NRG Oncology RTOG 9003. Results Both models included age, Zubrod performance status, pack-years, education, p16 status, and T and N stage; the OS model also included anemia and age × pack-years interaction; and the PFS model also included marital status, weight loss, and p16 × Zubrod interaction. Predictions correlated well with observed 2-year and 5-year outcomes. The uncorrected concordance index was 0.76 (95% CI, 0.72 to 0.80) for OS and 0.70 (95% CI, 0.66 to 0.74) for PFS, and bias-corrected indices were similar. In the validation set, OS and PFS models were well calibrated, and OS and PFS were significantly different across tertiles of nomogram scores (log-rank P = .003;\u3c .001). Conclusion The validated nomograms provided useful prediction of OS and PFS for patients with OPSCC treated with primary radiation-based therapy

The spike-timing-dependent learning rule to encode spatiotemporal patterns in a network of spiking neurons

We study associative memory neural networks based on the Hodgkin-Huxley type of spiking neurons. We introduce the spike-timing-dependent learning rule, in which the time window with the negative part as well as the positive part is used to describe the biologically plausible synaptic plasticity. The learning rule is applied to encode a number of periodical spatiotemporal patterns, which are successfully reproduced in the periodical firing pattern of spiking neurons in the process of memory retrieval. The global inhibition is incorporated into the model so as to induce the gamma oscillation. The occurrence of gamma oscillation turns out to give appropriate spike timings for memory retrieval of discrete type of spatiotemporal pattern. The theoretical analysis to elucidate the stationary properties of perfect retrieval state is conducted in the limit of an infinite number of neurons and shows the good agreement with the result of numerical simulations. The result of this analysis indicates that the presence of the negative and positive parts in the form of the time window contributes to reduce the size of crosstalk term, implying that the time window with the negative and positive parts is suitable to encode a number of spatiotemporal patterns. We draw some phase diagrams, in which we find various types of phase transitions with change of the intensity of global inhibition.Comment: Accepted for publication in Physical Review

Impact of cognitive stimulation on ripples within human epileptic and non-epileptic hippocampus

Background: Until now there has been no way of distinguishing between physiological and epileptic hippocampal ripples in intracranial recordings. In the present study we addressed this by investigating the effect of cognitive stimulation on interictal high frequency oscillations in the ripple range (80-250 Hz) within epileptic (EH) and non-epileptic hippocampus (NH). Methods: We analyzed depth EEG recordings in 10 patients with intractable epilepsy, in whom hippocampal activity was recorded initially during quiet wakefulness and subsequently during a simple cognitive task. Using automated detection of ripples based on amplitude of the power envelope, we analyzed ripple rate (RR) in the cognitive and resting period, within EH and NH. Results: Compared to quiet wakefulness we observed a significant reduction of RR during cognitive stimulation in EH, while it remained statistically marginal in NH. Further, we investigated the direct impact of cognitive stimuli on ripples (i.e. immediately post-stimulus), which showed a transient statistically significant suppression of ripples in the first second after stimuli onset in NH only. Conclusion: Our results point to a differential reactivity of ripples within EH and NH to cognitive stimulation