A review of stereotactic ablative radiotherapy for nonmetastatic renal cell carcinoma.

Renal cell carcinoma (RCC) is an uncommon malignancy whose incidence has been increasing over the past few decades, posing treatment challenges for elderly or infirm patients who are not surgical candidates. Stereotactic ablative radiotherapy (SABR) has emerged as a promising non-invasive treatment modality for RCC. The high dose-per-fraction used in SABR overcomes some of the mechanisms of radioresistance that has hindered the effective treatment of RCC with conventional radiotherapy. For primary RCC, local control rates for SABR exceed 90%, with typically minimal grade 3 or higher toxicities, offering a viable alternative for inoperable patients and those not eligible for or unable to tolerate radiofrequency or cryotherapy ablation. SABR can also be used in patients with a solitary kidney as a strategy for renal preservation to avoid need for dialysis. Given its excellent local control rates, low toxicity and preservation of renal function, SABR offers an attractive alternative to more invasive modalities for treatment of localized RCC.

Clinical and radiographic response of a paravertebral hemangioma to radiotherapy.

Hemangiomas can arise anywhere in the body. While vertebral hemangiomas are common, atypical hemangiomas with paraspinal and epidural extension are rare. We present a case of a patient who presented with persistent cough and anorexia from a paravertebral hemangioma that invaded the adjacent vertebrae and neural foramen causing moderate spinal canal stenosis. She was treated with stereotactic body radiotherapy to prevent the development of symptomatic spinal cord compression. The hemangioma underwent significant shrinkage and her cough resolved. This case demonstrates impressive and sustained clinical and radiographic response of a paraspinal hemangioma to stereotactic body radiotherapy.

Decoding biological age from face photographs using deep learning.

Because humans age at different rates, a person's physical appearance may yield insights into their biological age and physiological health more reliably than their chronological age. In medicine, however, appearance is incorporated into medical judgments in a subjective and non-standardized fashion. In this study, we developed and validated FaceAge, a deep learning system to estimate biological age from easily obtainable and low-cost face photographs. FaceAge was trained on data from 58,851 healthy individuals, and clinical utility was evaluated on data from 6,196 patients with cancer diagnoses from two institutions in the United States and The Netherlands. To assess the prognostic relevance of FaceAge estimation, we performed Kaplan Meier survival analysis. To test a relevant clinical application of FaceAge, we assessed the performance of FaceAge in end-of-life patients with metastatic cancer who received palliative treatment by incorporating FaceAge into clinical prediction models. We found that, on average, cancer patients look older than their chronological age, and looking older is correlated with worse overall survival. FaceAge demonstrated significant independent prognostic performance in a range of cancer types and stages. We found that FaceAge can improve physicians' survival predictions in incurable patients receiving palliative treatments, highlighting the clinical utility of the algorithm to support end-of-life decision-making. FaceAge was also significantly associated with molecular mechanisms of senescence through gene analysis, while age was not. These findings may extend to diseases beyond cancer, motivating using deep learning algorithms to translate a patient's visual appearance into objective, quantitative, and clinically useful measures.

Survival outcomes and predicting intracranial metastasis in stage III non-small cell lung cancer treated with definitive chemoradiation: Real-world data from a tertiary cancer center.

PURPOSE/OBJECTIVE: Around 30% of patients with non-small cell lung cancers (NSCLC) are diagnosed with stage III disease at presentation, of which about 50% are treated with definitive chemoradiation (CRT). Around 65-80% of patients will eventually develop intracranial metastases (IM), though associated risk factors are not clearly described. We report survival outcomes and risk factors for development of IM in a cohort of patients with stage III NSCLC treated with CRT at a tertiary cancer center. MATERIALS/METHODS: We identified 195 patients with stage III NSCLC treated with CRT from January 2010 to May 2021. Multivariable logistic regression was used to generate odds ratios for covariates associated with development of IM. Kaplan-Meier analysis with the Log Rank test was used for unadjusted time-to-event analyses. P-value for statistical significance was set at < 0.05 with a two-sided test. RESULTS: Out of 195 patients, 108 (55.4%) had stage IIIA disease and 103 (52.8%) had adenocarcinoma histology. The median age and follow-up (in months) was 67 (IQR 60-74) and 21 (IQR 12-43), respectively. The dose of radiation was 60 Gy in 30 fractions for148 patients (75.9%). Of the 77 patients who received treatment since immunotherapy was available and standard at our cancer center, 45 (58.4%) received at least one cycle. During follow-up, 84 patients (43.1%) developed any metastasis, and 33 (16.9%) developed IM (either alone or with extracranial metastasis). 150 patients (76.9%) experienced a treatment delay (interval between diagnosis and treatment > 4 weeks). Factors associated with developing any metastasis included higher overall stage at diagnosis (p = 0.013) and higher prescribed dose (p = 0.022). Factors associated with developing IM included higher ratio of involved over sampled lymph nodes (p = 0.001) and receipt of pre-CRT systemic or radiotherapy for any reason (p = 0.034). On multivariate logistical regression, treatment delay (OR 3.9, p = 0.036) and overall stage at diagnosis (IIIA vs. IIIB/IIIC) (OR 2.8, p = 0.02) predicted development of IM. These findings were sustained on sensitivity analysis using different delay intervals. Median OS was not reached for the overall cohort, and was 43.1 months for patients with IM and 40.3 months in those with extracranial-only metastasis (p = 0.968). In patients with any metastasis, median OS was longer (p = 0.003) for those who experienced a treatment delay (48.4 months) compared to those that did not (12.2 months), likely due to expedited diagnosis and treatment in patients with a higher symptom burden secondary to more advanced disease. CONCLUSIONS: In patients with stage III NSCLC treated with definitive CRT, the risk of IM appears to increase with overall stage at diagnosis and, importantly, may be associated with experiencing a treatment delay (> 4 weeks). Metastatic disease of any kind remains the primary life-limiting prognostic factor in these patients with advanced lung cancer. In patients with metastatic disease, treatment delay was associated with better survival. Patients who experience a treatment delay and those initially diagnosed at a more advanced overall stage may warrant more frequent surveillance for early diagnosis and treatment of IM. Healthcare system stakeholders should strive to mitigate treatment delay in patients with locally NSCLC to reduce the risk of IM. Further research is needed to better understand factors associated with survival, treatment delay, and the development of IM after CRT in the immunotherapy era.

Outcomes of Hypofractionated Stereotactic Radiotherapy for Small and Moderate-Sized Brain Metastases: A Single-Institution Analysis.

Background: Stereotactic radiosurgery (SRS) is the standard treatment for limited intracranial metastases. With the advent of frameless treatment delivery, fractionated stereotactic radiotherapy (FSRT) has become more commonly implemented given superior control and toxicity rates for larger lesions. We reviewed our institutional experience of FSRT to brain metastases without size restriction. Methods: We performed a retrospective review of our institutional database of patients treated with FSRT for brain metastases. Clinical and dosimetric details were abstracted. All patients were treated in 3 or 5 fractions using LINAC-based FSRT, did not receive prior cranial radiotherapy, and had at least 6 months of MRI follow-up. Overall survival was estimated using the Kaplan-Meier method. Local failure and radionecrosis cumulative incidence rates were estimated using a competing risks model with death as the competing risk. Univariable and multivariable analyses using Fine and Gray's proportional subdistribution hazards regression model were performed to determine covariates predictive of local failure and radionecrosis. Results: We identified 60 patients and 133 brain metastases treated at our institution from 2016 to 2020. The most common histologies were lung (53%) and melanoma (25%). Most lesions were >1 cm in diameter (84.2%) and did not have previous surgical resection (88%). The median duration of imaging follow-up was 9.8 months. The median survival for the whole cohort was 20.5 months. The local failure at 12 months was 17.8% for all lesions, 22.1% for lesions >1 cm, and 13.7% for lesions ≤1 cm (p = 0.36). The risk of radionecrosis at 12 months was 7.1% for all lesions, 13.2% for lesions >1 cm, and 3.2% for lesions ≤1 cm (p = 0.15). Conclusions: FSRT is safe and effective in the treatment of brain metastases of any size with excellent local control and toxicity outcomes. Prospective evaluation against single-fraction SRS is warranted for all lesion sizes.

Adjuvant Radiotherapy for Upper Tract Urothelial Carcinoma: Systematic Review and Meta-Analysis.

PURPOSE: Upper tract urothelial carcinoma (UTUC) is a rare form of malignancy comprising only 5% of urothelial cancers. The mainstay of treatment is radical nephroureterectomy (RNU) with bladder cuff excision. Neoadjuvant or adjuvant chemotherapy is often used in locally advanced disease. The role of adjuvant radiotherapy (RT), however, remains controversial. To further explore the potential role of adjuvant RT, we performed a systematic review and meta-analysis of the literature from 1990 to present. METHODS AND MATERIALS: We identified 810 candidate articles from database searches, of which 67 studies underwent full-text review, with final inclusion of 20 eligible studies. Among the included studies, there were no randomized controlled trials and a single prospective trial, with the remainder being retrospective series. We performed quantitative synthesis of the results by calculating the pooled odds ratios (OR) for the primary outcome of locoregional recurrence (LRR) and secondary outcomes of overall survival (OS), cancer-specific survival (CSS) and distant recurrence (DR). RESULTS: Adjuvant RT, which was mostly prescribed for locally advanced or margin-positive disease following RNU, significantly reduced locoregional recurrence risk OR 0.43 (95% CI: 0.23-0.70), and the effect remained significant even following subgroup analysis to account for adjuvant systemic therapy. The effect of adjuvant RT on 3-year OS, 5-year CSS and DR was non-significant. However, 5-year OS was unfavourable in the adjuvant RT arm, but study heterogeneity was high, and analysis of small-study effects and subgroups suggested bias in reporting of outcomes. CONCLUSIONS: Adjuvant RT in the setting of locally advanced UTUC improves locoregional control following definitive surgery, but does not appear to improve OS. Higher-quality studies, ideally randomized controlled trials, are needed to further quantify its benefit in this setting, and to explore multi-modal treatments that include systemic agents given concomitantly or sequentially with RT, which may offer an OS benefit in addition to the locoregional control benefit of RT.

Blind method for discovering number of clusters in multidimensional datasets by regression on linkage hierarchies generated from random data.

Determining intrinsic number of clusters in a multidimensional dataset is a commonly encountered problem in exploratory data analysis. Unsupervised clustering algorithms often rely on specification of cluster number as an input parameter. However, this is typically not known a priori. Many methods have been proposed to estimate cluster number, including statistical and information-theoretic approaches such as the gap statistic, but these methods are not always reliable when applied to non-normally distributed datasets containing outliers or noise. In this study, I propose a novel method called hierarchical linkage regression, which uses regression to estimate the intrinsic number of clusters in a multidimensional dataset. The method operates on the hypothesis that the organization of data into clusters can be inferred from the hierarchy generated by partitioning the dataset, and therefore does not directly depend on the specific values of the data or their distribution, but on their relative ranking within the partitioned set. Moreover, the technique does not require empirical data to train on, but can use synthetic data generated from random distributions to fit regression coefficients. The trained hierarchical linkage regression model is able to infer cluster number in test datasets of varying complexity and differing distributions, for image, text and numeric data, using the same regression model without retraining. The method performs favourably against other cluster number estimation techniques, and is also robust to parameter changes, as demonstrated by sensitivity analysis. The apparent robustness and generalizability of hierarchical linkage regression make it a promising tool for unsupervised exploratory data analysis and discovery.

Response evaluation after stereotactic ablative radiotherapy for localised non-small-cell lung cancer: an equipoise of available resource and accuracy.

There is growing evidence supporting the use of stereotactic ablative radiotherapy (SABR) on the treatment of localised stage non-small-cell lung cancer (NSCLC). Distinctive imaging challenges are posed post-SABR treatment. Thus, it is imperative to provide guidance on assessing treatment response, especially for new adopters. This commentary is about filling a gap in response evaluation after SABR for localised NSCLC.

Mapping the coherence of ictal high frequency oscillations in human extratemporal lobe epilepsy.

OBJECTIVE: High frequency oscillations (HFOs) have recently been recorded in epilepsy patients and proposed as possible novel biomarkers of epileptogenicity. Investigation of additional HFO characteristics that correlate with the clinical manifestation of seizures may yield additional insights for delineating epileptogenic regions. To that end, this study examined the spatiotemporal coherence patterns of HFOs (80-400 Hz) so as to characterize the strength of HFO interactions in the epileptic brain. We hypothesized that regions of strong HFO coherence identified epileptogenic networks believed to possess a pathologic locking nature in relation to regular brain activity. METHODS: We applied wavelet phase coherence analysis to the intracranial EEG (iEEG)s of patients (n = 5) undergoing presurgical evaluation of drug-resistant extratemporal lobe epilepsy (ETLE). We have also computed HFO intensity (related to the square-root of the power), to study the relationship between HFO amplitude and coherence. RESULTS: Strong HFO (80-270 Hz) coherence was observed in a consistent and spatially focused channel cluster during seizures in four of five patients. Furthermore, cortical regions possessing strong ictal HFO coherence coincided with regions exhibiting high ictal HFO intensity, relative to all other channels. SIGNIFICANCE: Because HFOs have been shown to localize to the epileptogenic zone, and we have demonstrated a correlation between ictal HFO intensity and coherence, we propose that ictal HFO coherence can act as an epilepsy biomarker. Moreover, the seizures studied here showed strong spatial correlation of ictal HFO coherence and intensity in the 80-270 Hz frequency range, suggesting that this band may be targeted when defining seizure-related regions of interest for characterizing ETLE.

Synchrony of high frequency oscillations in the human epileptic brain.

We have applied wavelet phase coherence (WPC) to human iEEG data to characterize the spatial and temporal interactions of high frequency oscillations (HFOs; >80 Hz). Quantitative analyses were performed on iEEG segments from four patients with extratemporal lobe epilepsy. Interelectrode synchrony was measured using WPC before, during and after seizure activity. The WPC profiles of HFOs were able to elucidate the seizure from non-seizure state in all four patients and for all seizures studied (n=10). HFO synchrony was consistently transient and of weak to moderate strength during non-seizure activity, while weak to very strong coherence, of prolonged duration, was observed during seizures. Several studies have suggested that HFOs may have a significant role in the process of epileptogenesis and seizure genesis. As epileptic seizures result from disturbances in the regular electrical activity present in given areas of the brain, studying the interactions between fast brain waves, recorded simultaneously and from many different brain regions, may provide more information of which brain areas are interacting during ictal and interictal activity.

Synthesis of high-complexity rhythmic signals for closed-loop electrical neuromodulation.

We propose an approach to synthesizing high-complexity rhythmic signals for closed-loop electrical neuromodulation using cognitive rhythm generator (CRG) networks, wherein the CRG is a hybrid oscillator comprised of (1) a bank of neuronal modes, (2) a ring device (clock), and (3) a static output nonlinearity (mapper). Networks of coupled CRGs have been previously implemented to simulate the electrical activity of biological neural networks, including in silico models of epilepsy, producing outputs of similar waveform and complexity to the biological system. This has enabled CRG network models to be used as platforms for testing seizure control strategies. Presently, we take the application one step further, envisioning therapeutic CRG networks as rhythmic signal generators creating neuromimetic signals for stimulation purposes, motivated by recent research indicating that stimulus complexity and waveform characteristics influence neuromodulation efficacy. To demonstrate this concept, an epileptiform CRG network generating spontaneous seizure-like events (SLEs) was coupled to a therapeutic CRG network, forming a closed-loop neuromodulation system. SLEs are associated with low-complexity dynamics and high phase coherence in the network. The tuned therapeutic network generated a high-complexity, multi-banded rhythmic stimulation signal with prominent theta and gamma-frequency power that suppressed SLEs and increased dynamic complexity in the epileptiform network, as measured by a relative increase in the maximum Lyapunov exponent and decrease in phase coherence. CRG-based neuromodulation outperformed both low and high-frequency periodic pulse stimulation, suggesting that neuromodulation using complex, biomimetic signals may provide an improvement over conventional electrical stimulation techniques for treating neurological disorders such as epilepsy.

Markers of pathological excitability derived from principal dynamic modes of hippocampal neurons.

Transformation of principal dynamic modes (PDMs) under epileptogenic conditions was investigated by computing the Volterra kernels in a rodent epilepsy model derived from a mouse whole hippocampal preparation, where epileptogenesis was induced by altering the concentrations of Mg(2 +) and K(+) of the perfusate for different levels of excitability. Both integrating and differentiating PDMs were present in the neuronal dynamics, and both of them increased in absolute magnitude for increased excitability levels. However, the integrating PDMs dominated at all levels of excitability in terms of their relative contributions to the overall response, whereas the dominant frequency responses of the differentiating PDMs were shifted to higher ranges under epileptogenic conditions, from ripple activities (75-200 Hz) to fast ripple activities (200-500 Hz).

Responsive neuromodulators based on artificial neural networks used to control seizure-like events in a computational model of epilepsy.

Deep brain stimulation (DBS) has been noted for its potential to suppress epileptic seizures. To date, DBS has achieved mixed results as a therapeutic approach to seizure control. Using a computational model, we demonstrate that high-complexity, biologically-inspired responsive neuromodulation is superior to periodic forms of neuromodulation (responsive and non-responsive) such as those implemented in DBS, as well as neuromodulation using random and random repetitive-interval stimulation. We configured radial basis function (RBF) networks to generate outputs modeling interictal time series recorded from rodent hippocampal slices that were perfused with low Mg²âº/high K⁺ solution. We then compared the performance of RBF-based interictal modulation, periodic biphasic-pulse modulation, random modulation and random repetitive modulation on a cognitive rhythm generator (CRG) model of spontaneous seizure-like events (SLEs), testing efficacy of SLE control. A statistically significant improvement in SLE mitigation for the RBF interictal modulation case versus the periodic and random cases was observed, suggesting that the use of biologically-inspired neuromodulators may achieve better results for the purpose of electrical control of seizures in a clinical setting.

Complexity in neuronal noise depends on network interconnectivity.

"Noise," or noise-like activity (NLA), defines background electrical membrane potential fluctuations at the cellular level of the nervous system, comprising an important aspect of brain dynamics. Using whole-cell voltage recordings from fast-spiking stratum oriens interneurons and stratum pyramidale neurons located in the CA3 region of the intact mouse hippocampus, we applied complexity measures from dynamical systems theory (i.e., 1/f(γ) noise and correlation dimension) and found evidence for complexity in neuronal NLA, ranging from high- to low-complexity dynamics. Importantly, these high- and low-complexity signal features were largely dependent on gap junction and chemical synaptic transmission. Progressive neuronal isolation from the surrounding local network via gap junction blockade (abolishing gap junction-dependent spikelets) and then chemical synaptic blockade (abolishing excitatory and inhibitory post-synaptic potentials), or the reverse order of these treatments, resulted in emergence of high-complexity NLA dynamics. Restoring local network interconnectivity via blockade washout resulted in resolution to low-complexity behavior. These results suggest that the observed increase in background NLA complexity is the result of reduced network interconnectivity, thereby highlighting the potential importance of the NLA signal to the study of network state transitions arising in normal and abnormal brain dynamics (such as in epilepsy, for example).

Frequency interactions in human epileptic brain.

We have used two algorithms, wavelet phase coherence (WPC) and modulation index (MI) analysis to study frequency interactions in the human epileptic brain. Quantitative analyses were performed on intracranial electroencephalographic (iEEG) segments from three patients with neocortical epilepsy. Interelectrode coherence was measured using WPC and intraelectrode frequency interactions were analyzed using MI. WPC was performed on electrode pairings and the temporal evolution of phase couplings in the following frequency ranges: 1-4 Hz, 4-8 Hz, 8-13 Hz, 13-30 Hz and 30-100 Hz was studied. WPC was strongest in the 1-4 Hz frequency range during both seizure and non-seizure activities; however, WPC values varied minimally between electrode pairings. The 13-30 Hz band showed the lowest WPC values during seizure activity. MI analysis yielded two prominent patterns of frequency-specific activity, during seizure and non-seizure activities, which were present across all patients.

Spectral features of electroencephalogram in characterizing various brain states under anesthesia.

The administration of the anesthetic agents is known to alter the electroencephalogram (EEG) signal significantly with the brain being their primary target. In this study, we analyzed the EEG recorded from six ASA I/II patients undergoing a 1-2 hour surgery. The EEG was collected before and during induction, maintenance and recovery of anesthesia using the 10/20 lead-system. A combination of fentanyl and propofol (± rocuronium) was used for induction and a Sevoflurane in air/O(2) mixture was administered through an endotracheal tube to achieve the steady minimum alveolar concentration (MAC). This study showed that 0 to 4 Hz signal power was most sensitive to the changes associated with induction of anesthesia whereas the 4 to 12 Hz power was important in classifying states during maintenance of anesthesia. Anesthesia also promoted heightened phase coherence in 8 to 16 Hz and 16 to 30 Hz ranges during maintenance and induction of anesthesia, respectively. Additionally, strong cross-frequency coupling between 7 to 20 Hz and 10 to 40 Hz was observed during anesthesia suggesting alteration of neural coding.

System characterization of neuronal excitability in the hippocampus and its relevance to observed dynamics of spontaneous seizure-like transitions.

Most forms of epilepsy are marked by seizure episodes that arise spontaneously. The low-magnesium/high-potassium (low-Mg(2+)/high-K(+)) experimental model of epilepsy is an acute model that produces spontaneous, recurring seizure-like events (SLEs). To elucidate the nature of spontaneous seizure transitions and their relationship to neuronal excitability, whole-cell recordings from the intact hippocampus were undertaken in vitro, and the response of hippocampal CA3 neurons to Gaussian white noise injection was obtained before and after treatment with various concentrations of low-Mg(2+)/high-K(+) solution. A second-order Volterra kernel model was estimated for each of the input-output response pairs. The spectral energy of the responses was also computed, providing a quantitative measure of neuronal excitability. Changes in duration and amplitude of the first-order kernel correlated positively with the spectral energy increase following treatment with low-Mg(2+)/high-K(+) solution, suggesting that variations in neuronal excitability are coded by the system kernels, in part by differences to the profile of the first-order kernel. In particular, kernel duration was more sensitive than amplitude to changes in spectral energy, and correlated more strongly with kernel area. An oscillator network model of the hippocampal CA3 was constructed to investigate the relationship of kernel duration to network excitability, and the model was able to generate spontaneous, recurrent SLEs by increasing the duration of a mode function analogous to the first-order kernel. Results from the model indicated that disruption to the dynamic balance of feedback was responsible for seizure-like transitions and the observed intermittency of SLEs. A physiological candidate for feedback imbalance consistent with the network model is the destabilizing interaction of extracellular potassium and paroxysmal neuronal activation. Altogether, these results (1) validate a mathematical model for epileptiform activity in the hippocampus by quantifying and subsequently correlating its behavior with an experimental, in vitro model of epilepsy; (2) elucidate a possible mechanism for epileptogenesis; and (3) pave the way for control studies in epilepsy utilizing the herein proposed experimental and mathematical setup.

Transformation of neuronal modes associated with low-Mg2+/high-K+ conditions in an in vitro model of epilepsy.

Nonparametric system modeling constitutes a robust method for the analysis of physiological systems as it can be used to identify nonlinear dynamic input-output relationships and facilitate their description. First- and second-order kernels of hippocampal CA3 pyramidal neurons in an in vitro slice preparation were computed using the Volterra-Wiener approach to investigate system changes associated with epileptogenic low-magnesium/high-potassium (low-Mg(2+)/high-K(+)) conditions. The principal dynamic modes (PDMs) of neurons were calculated from the first- and second-order kernel estimates in order to characterize changes in neural coding functionality. From our analysis, an increase in nonlinear properties was observed in kernels under low-Mg(2+)/high-K(+). Furthermore, the PDMs revealed that the sampled hippocampal CA3 neurons were primarily of integrating character and that the contribution of a differentiating mode component was enhanced under low-Mg(2+)/high-K(+).

Theta phase precession and phase selectivity: a cognitive device description of neural coding.

Information in neural systems is carried by way of phase and rate codes. Neuronal signals are processed through transformative biophysical mechanisms at the cellular and network levels. Neural coding transformations can be represented mathematically in a device called the cognitive rhythm generator (CRG). Incoming signals to the CRG are parsed through a bank of neuronal modes that orchestrate proportional, integrative and derivative transformations associated with neural coding. Mode outputs are then mixed through static nonlinearities to encode (spatio) temporal phase relationships. The static nonlinear outputs feed and modulate a ring device (limit cycle) encoding output dynamics. Small coupled CRG networks were created to investigate coding functionality associated with neuronal phase preference and theta precession in the hippocampus. Phase selectivity was found to be dependent on mode shape and polarity, while phase precession was a product of modal mixing (i.e. changes in the relative contribution or amplitude of mode outputs resulted in shifting phase preference). Nonlinear system identification was implemented to help validate the model and explain response characteristics associated with modal mixing; in particular, principal dynamic modes experimentally derived from a hippocampal neuron were inserted into a CRG and the neuron's dynamic response was successfully cloned. From our results, small CRG networks possessing disynaptic feedforward inhibition in combination with feedforward excitation exhibited frequency-dependent inhibitory-to-excitatory and excitatory-to-inhibitory transitions that were similar to transitions seen in a single CRG with quadratic modal mixing. This suggests nonlinear modal mixing to be a coding manifestation of the effect of network connectivity in shaping system dynamic behavior. We hypothesize that circuits containing disynaptic feedforward inhibition in the nervous system may be candidates for interpreting upstream rate codes to guide downstream processes such as phase precession, because of their demonstrated frequency-selective properties.

A wavelet packet-based algorithm for the extraction of neural rhythms.

Neural rhythms are associated with different brain functions and pathological conditions. These rhythms are often clinically relevant for purposes of diagnosis or treatment, though their complex, time-varying features make them difficult to isolate. The wavelet packet transform has proven itself to be versatile and effective with respect to resolving signal features in both time and frequency. We propose a signal analysis technique, called neural rhythm extraction (NRE) that incorporates wavelet packet analysis along with a threshold-based scheme for separating rhythmic neural features from non-rhythmic ones. We applied NRE to rat in vitro intracellular recordings and human scalp electroencephalogram (EEG) signals, and were able to isolate and classify individual neural rhythms in signals containing large amplitude spikes and other artifacts. NRE is capable of discriminating signal features sharing similar time or frequency localization, as well as extracting low-amplitude, low-power rhythms otherwise masked by spectrally dominant signal components. The algorithm allows for independent retention and reconstruction of rhythmic features, which may serve to enhance other analysis techniques such as independent component analysis (ICA), and aid in application-specific tasks such as detection, classification or tracking.