Long-term Effect of Multichannel tDCS Protocol in Patients with Central Cortex Epilepsies Associated with Epilepsia Partialis Continua.

Epilepsia partialis continua (EPC) is a rare type of focal motor status epilepticus that causes continuous muscle jerking in a specific part of the body. Experiencing this type of seizure, along with other seizure types, such as focal motor seizures and focal to bilateral tonic-clonic seizures, can result in a disabling situation. Non-invasive brain stimulation methods like transcranial direct current stimulation (tDCS) show promise in reducing seizure frequency (SF) when medications are ineffective. However, research on tDCS for EPC and related seizures is limited. We evaluated personalized multichannel tDCS in drug-resistant EPC of diverse etiologies for long-term clinical efficacy We report three EPC patients undergoing a long-term protocol of multichannel tDCS. The patients received several cycles (11, 9, and 3) of five consecutive days of stimulation at 2 mA for 2 × 20 min, targeting the epileptogenic zone (EZ), including the central motor cortex with cathodal electrodes. The primary measurement was SF changes. In three cases, EPC was due to Rasmussen's Encephalitis (case 1), focal cortical dysplasia (case 2), or remained unknown (case 3). tDCS cycles were administered over 6 to 22 months. The outcomes comprised a reduction of at least 75% in seizure frequency for two patients, and in one case, a complete cessation of severe motor seizures. However, tDCS had no substantial impact on the continuous myoclonus characterizing EPC. No serious side effects were reported. Long-term application of tDCS cycles is well tolerated and can lead to a considerable reduction in disabling seizures in patients with various forms of epilepsy with EPC.

Complex patterns of spatially extended generators of epileptic activity: Comparison of source localization methods cMEM and 4-ExSo-MUSIC on high resolution EEG and MEG data.

Electric Source Imaging (ESI) and Magnetic Source Imaging (MSI) of EEG and MEG signals are widely used to determine the origin of interictal epileptic discharges during the pre-surgical evaluation of patients with epilepsy. Epileptic discharges are detectable on EEG/MEG scalp recordings only when associated with a spatially extended cortical generator of several square centimeters, therefore it is essential to assess the ability of source localization methods to recover such spatial extent. In this study we evaluated two source localization methods that have been developed for localizing spatially extended sources using EEG/MEG data: coherent Maximum Entropy on the Mean (cMEM) and 4th order Extended Source Multiple Signal Classification (4-ExSo-MUSIC). In order to propose a fair comparison of the performances of the two methods in MEG versus EEG, this study considered realistic simulations of simultaneous EEG/MEG acquisitions taking into account an equivalent number of channels in EEG (257 electrodes) and MEG (275 sensors), involving a biophysical computational neural mass model of neuronal discharges and realistically shaped head models. cMEM and 4-ExSo-MUSIC were evaluated for their sensitivity to localize complex patterns of epileptic discharges which includes (a) different locations and spatial extents of multiple synchronous sources, and (b) propagation patterns exhibited by epileptic discharges. Performance of the source localization methods was assessed using a detection accuracy index (Area Under receiver operating characteristic Curve, AUC) and a Spatial Dispersion (SD) metric. Finally, we also presented two examples illustrating the performance of cMEM and 4-ExSo-MUSIC on clinical data recorded using high resolution EEG and MEG. When simulating single sources at different locations, both 4-ExSo-MUSIC and cMEM exhibited excellent performance (median AUC significantly larger than 0.8 for EEG and MEG), whereas, only for EEG, 4-ExSo-MUSIC showed significantly larger AUC values than cMEM. On the other hand, cMEM showed significantly lower SD values than 4-ExSo-MUSIC for both EEG and MEG. When assessing the impact of the source spatial extent, both methods provided consistent and reliable detection accuracy for a wide range of source spatial extents (source sizes ranging from 3 to 20cm2 for MEG and 3 to 30cm2 for EEG). For both EEG and MEG, 4-ExSo-MUSIC localized single source of large signal-to-noise ratio better than cMEM. In the presence of two synchronous sources, cMEM was able to distinguish well the two sources (their location and spatial extent), while 4-ExSo-MUSIC only retrieved one of them. cMEM was able to detect the spatio-temporal propagation patterns of two synchronous activities while 4-ExSo-MUSIC favored the strongest source activity. Overall, in the context of localizing sources of epileptic discharges from EEG and MEG data, 4-ExSo-MUSIC and cMEM were found accurately sensitive to the location and spatial extent of the sources, with some complementarities. Therefore, they are both eligible for application on clinical data.

EEG extended source localization: tensor-based vs. conventional methods.

The localization of brain sources based on EEG measurements is a topic that has attracted a lot of attention in the last decades and many different source localization algorithms have been proposed. However, their performance is limited in the case of several simultaneously active brain regions and low signal-to-noise ratios. To overcome these problems, tensor-based preprocessing can be applied, which consists in constructing a space-time-frequency (STF) or space-time-wave-vector (STWV) tensor and decomposing it using the Canonical Polyadic (CP) decomposition. In this paper, we present a new algorithm for the accurate localization of extended sources based on the results of the tensor decomposition. Furthermore, we conduct a detailed study of the tensor-based preprocessing methods, including an analysis of their theoretical foundation, their computational complexity, and their performance for realistic simulated data in comparison to conventional source localization algorithms such as sLORETA, cortical LORETA (cLORETA), and 4-ExSo-MUSIC. Our objective consists, on the one hand, in demonstrating the gain in performance that can be achieved by tensor-based preprocessing, and, on the other hand, in pointing out the limits and drawbacks of this method. Finally, we validate the STF and STWV techniques on real measurements to demonstrate their usefulness for practical applications.

Detection of seizure onset in childhood absence epilepsy.

OBJECTIVE: This study aims to detect the seizure onset, in childhood absence epilepsy, as early as possible. Indeed, interfering with absence seizures with sensory simulation has been shown to be possible on the condition that the stimulation occurs soon enough after the seizure onset. METHODS: We present four variations (two supervised, two unsupervised) of an algorithm designed to detect the onset of absence seizures from 4 scalp electrodes, and compare their performance with that of a state-of-the-art algorithm. We exploit the characteristic shape of spike-wave discharges to detect the seizure onset. Their performance is assessed on clinical electroencephalograms from 63 patients with confirmed childhood absence epilepsy. RESULTS: The proposed approaches succeed in early detection of the seizure onset, contrary to the classical detection algorithm. Indeed, the results clearly show the superiority of the proposed methods for small delays of detection, under 750 ms from the onset. CONCLUSION: The performance of the proposed unsupervised methods is equivalent to that of the supervised ones. The use of only four electrodes makes the pipeline suitable to be embedded in a wearable device. SIGNIFICANCE: The proposed pipelines perform early detection of absence seizures, which constitutes a prerequisite for a closed-loop system.

NeoCoMM: A neocortical neuroinspired computational model for the reconstruction and simulation of epileptiform events.

BACKGROUND: Understanding the pathophysiological dynamics that underline Interictal Epileptiform Events (IEEs) such as epileptic spikes, spike-and-waves or High-Frequency Oscillations (HFOs) is of major importance in the context of neocortical refractory epilepsy, as it paves the way for the development of novel therapies. Typically, these events are detected in Local Field Potential (LFP) recordings obtained through depth electrodes during pre-surgical investigations. Although essential, the underlying pathophysiological mechanisms for the generation of these epileptic neuromarkers remain unclear. The aim of this paper is to propose a novel neurophysiologically relevant reconstruction of the neocortical microcircuitry in the context of epilepsy. This reconstruction intends to facilitate the analysis of a comprehensive set of parameters encompassing physiological, morphological, and biophysical aspects that directly impact the generation and recording of different IEEs. METHOD: a novel microscale computational model of an epileptic neocortical column was introduced. This model incorporates the intricate multilayered structure of the cortex and allows for the simulation of realistic interictal epileptic signals. The proposed model was validated through comparisons with real IEEs recorded using intracranial stereo-electroencephalography (SEEG) signals from both humans and animals. Using the model, the user can recreate epileptiform patterns observed in different species (human, rodent, and mouse) and study the intracellular activity associated with these patterns. RESULTS: Our model allowed us to unravel the relationship between glutamatergic and GABAergic synaptic transmission of the epileptic neural network and the type of generated IEE. Moreover, sensitivity analyses allowed for the exploration of the pathophysiological parameters responsible for the transitions between these events. Finally, the presented modeling framework also provides an Electrode Tissue Model (ETI) that adds realism to the simulated signals and offers the possibility of studying their sensitivity to the electrode characteristics. CONCLUSION: The model (NeoCoMM) presented in this work can be of great use in different applications since it offers an in silico framework for sensitivity analysis and hypothesis testing. It can also be used as a starting point for more complex studies.

Improving Fast Ripples Recording With Model-Guided Design of Microelectrodes.

OBJECTIVE: Microelectrodes allow the recording of neural activities with a high spatial resolution. However, their small sizes result in high impedance causing high thermal noise and poor signal-to-noise ratio. In drug-resistant epilepsy, the accurate detection of Fast Ripples (FRs) can help in the identification of epileptogenic networks. Consequently, good-quality recordings are instrumental in improving surgical outcomes. In this work, we propose a novel model-based approach for the design of microelectrodes optimized for FRs recording. METHODS: A 3D microscale computational model was developed to simulate FRs generated in the hippocampus. It was coupled with a model of the Electrode-Tissue Interface that accounts for the biophysical properties of intracortical microelectrode. This hybrid model was used to analyze the microelectrode geometrical and physical characteristics and their impact on recorded FRs. For model validation, experimental signals (local field potentials, LFPs) were recorded from CA1 using different electrode materials: stainless steel, gold, and gold coated with poly(3,4-ethylene dioxythiophene)/Poly(styrene sulfonate) (Au:PEDOT/PSS). RESULTS: results indicated that a radius between 65 and 120 µm for a wire microelectrode is the most optimal for recording FRs. In addition, in silico and in vivo quantified results showed a possible improvement in FRs observability using PEDOT/PSS coated microelectrodes. CONCLUSION: the optimization of the design of microelectrodes for FRs recording can improve the observability and detectability of FRs which are a recognized marker of epileptogenicity. SIGNIFICANCE: This model-based approach can assist in the design of hybrid electrodes that can be used in the presurgical evaluation of epileptic patients with drug-resistant epilepsy.

Recording of fast activity at the onset of partial seizures: depth EEG vs. scalp EEG.

Rapid discharges (25-80 Hz), a characteristic EEG pattern often recorded at seizure onset in partial epilepsies, are often considered as electrophysiological signatures of the epileptogenic zone. While the recording of rapid discharges from intracranial electrodes has long been established, their observation from the scalp is challenging. The prevailing view is that rapid discharges cannot be seen clearly (or at all) in scalp EEG because they have low signal-to-noise ratio. To date, however, no studies have investigated the 'observability' of rapid discharges, i.e. under what conditions and to what extent they can be visible in recorded EEG signals. Here, we used a model-based approach to examine the impact of several factors (distance to sources, skull conductivity, source area, source synchrony, and background activity) on the observability of rapid discharges in simultaneously simulated depth EEG and scalp EEG signals. In our simulations, the rapid discharge was clearly present in depth EEG signals but mostly almost not visible in scalp EEG signals. We identified some of the factors that may limit the observability of the rapid discharge on the scalp. Notably, surrounding background activity was found to be the most critical factor. The findings are discussed in relation to the presurgical evaluation of epilepsy.

HD-EEG for tracking sub-second brain dynamics during cognitive tasks.

This work provides the community with high-density Electroencephalography (HD-EEG, 256 channels) datasets collected during task-free and task-related paradigms. It includes forty-three healthy participants performing visual naming and spelling tasks, visual and auditory naming tasks and a visual working memory task in addition to resting state. The HD-EEG data are furnished in the Brain Imaging Data Structure (BIDS) format. These datasets can be used to (i) track brain networks dynamics and their rapid reconfigurations at sub-second time scale in different conditions, (naming/spelling/rest) and modalities, (auditory/visual) and compare them to each other, (ii) validate several parameters involved in the methods used to estimate cortical brain networks through scalp EEG, such as the open question of optimal number of channels and number of regions of interest and (iii) allow the reproducibility of results obtained so far using HD-EEG. We hope that delivering these datasets will lead to the development of new methods that can be used to estimate brain cortical networks and to better understand the general functioning of the brain during rest and task. Data are freely available from https://openneuro.org .

Effect of connectivity measures on the identification of brain functional core network at rest.

Magneto/Electro-encephalography (M/EEG) source connectivity is an emergent tool to identify brain networks with high time/space resolution. Here, we aim to identify the brain core network (s-core decomposition) using dense-EEG. We also evaluate the effect of the functional connectivity methods used and more precisely the effect of the correction for the so-called source leakage problem. Two connectivity measures were evaluated: the phase locking value (PLV) and phase lag index (PLI) that supposed to deal with the leakage problem by removing the zero-lag connections. Both methods were evaluated on resting state dense-EEG signals recorded from 19 healthy participants. Core networks obtained by each method was compared to those computed using fMRI from 487 healthy participants at rest (from the Human Connectome Project - HCP). The correlation between networks obtained by EEG and fMRI was used as performance criterion. Results show that PLV networks are closer to fMRI networks with significantly higher correlation values with fMRI networks, than PLI networks. Results suggest caution when selecting the functional connectivity methods and mainly methods that remove the zero-lag connections as it can severely affect the network characteristics. The choice of functional connectivity measure is indeed crucial not only in cognitive neuroscience but also in clinical neuroscience.

The neuronal sources of EEG: modeling of simultaneous scalp and intracerebral recordings in epilepsy.

In many applications which make use of EEG to investigate brain functions, a central question is often to relate the recorded signals to the spatio-temporal organization of the underlying neuronal sources of activity. A modeling attempt to quantitatively investigate this imperfectly known relationship is reported. The proposed plausible model of EEG generation relies on an accurate representation of the neuronal sources of activity. It combines both an anatomically realistic description of the spatial features of the sources (convoluted dipole layer) and a physiologically relevant description of their temporal activities (coupled neuronal populations). The model was used in the particular context of epileptiform activity (interictal spikes) to interpret simultaneously generated scalp and intracerebral EEG. Its integrative properties allowed for the bridging between source-related parameters (spatial extent, location, synchronization) and the properties of resulting EEG signals (amplitude of spikes, amplitude gradient along intracerebral electrodes, topography over scalp electrodes). The sensitivity of both recording modalities to source-related parameters was also studied. The model confirmed that the cortical area involved in interictal spikes is rather large. Its relative location with respect to recording electrodes was found to strongly influence the properties of EEG signals as the source geometry is a critical parameter. The influence, on simulated signals, of the synchronization degree between neuronal populations within the epileptic source was also investigated. The model revealed that intracerebral EEG can reflect epileptic activities corresponding to weak synchronization between neuronal populations of the epileptic patch. These results, as well as the limitations of the model, are discussed.

[The concept of an epileptogenic network in human partial epilepsies]. / Le concept de réseau épileptogène dans les épilepsies partielles humaines.

An anatomical and functional model of drug-resistant partial seizures is presented and discussed based on research conducted by our team over the last decade. This research is based on the study of intracerebral stereoelectroencephalography (SEEG) recordings in an attempt to identify the neural networks involved in generating paroxystic activities so as to understand their dynamics in space and time, and to propose targeted therapies that could "control" these networks. Today, the classical notion of epileptic focus should be replaced by a more complex model that takes into account the potential interactions within the neuronal networks involved in seizures. During partial epileptic seizures, the cerebral structures involved are the seat of characteristic oscillations that may be synchronized or, on the contrary, that can desynchronize in a transitory manner. These epileptic rhythms disturb the physiological rhythms that underlie the cognitive and emotional processes, which can thus be altered in partial epilepsy, even if located far from the original discharge site. We suggest that seizures originate in a group of structures that are highly epileptogenic (epileptogenic zone network, [EZN]) whose activity is synchronized before the appearance of fast oscillations that are transitorily desynchronized. Later, other cortical and subcortical structures are the seat of slower, synchronized rhythmic modifications (propagation network, [PN]). The emergence of clinical signs in the seizure depend on these phenomena, which in some cases can mimic a normal cognitive process or, on the contrary, lead to a deep rupture in normal cerebral functioning.

Dynamic reshaping of functional brain networks during visual object recognition.

OBJECTIVE: Emerging evidence shows that the modular organization of the human brain allows for better and efficient cognitive performance. Many of these cognitive functions are very fast and occur in a sub-second time scale such as the visual object recognition. APPROACH: Here, we investigate brain network modularity while controlling stimuli meaningfulness and measuring a participant's reaction time. We particularly raised two questions: i) does the dynamic brain network modularity change during the recognition of meaningful and meaningless visual images? And (ii) is there a correlation between network modularity and the reaction time of the participants? To tackle these issues, we collected dense-electroencephalography (EEG, 256 channels) data from 20 healthy human subjects performing a cognitive task consisting of naming meaningful (tools, animals…) and meaningless (scrambled) images. Functional brain networks in both categories were estimated at the sub-second time scale using the EEG source connectivity method. By using multislice modularity algorithms, we tracked the reconfiguration of functional networks during the recognition of both meaningful and meaningless images. MAIN RESULTS: Results showed a difference in the module's characteristics of both conditions in term of integration (interactions between modules) and occurrence (probability on average of any two brain regions to fall in the same module during the task). Integration and occurrence were greater for meaningless than for meaningful images. Our findings revealed also that the occurrence within the right frontal regions and the left occipito-temporal can help to predict the ability of the brain to rapidly recognize and name visual stimuli. SIGNIFICANCE: We speculate that these observations are applicable not only to other fast cognitive functions but also to detect fast disconnections that can occur in some brain disorders.

Reduced integration and improved segregation of functional brain networks in Alzheimer's disease.

OBJECTIVE: Emerging evidence shows that cognitive deficits in Alzheimer's disease (AD) are associated with disruptions in brain functional connectivity. Thus, the identification of alterations in AD functional networks has become a topic of increasing interest. However, to what extent AD induces disruption of the balance of local and global information processing in the human brain remains elusive. The main objective of this study is to explore the dynamic topological changes of AD networks in terms of brain network segregation and integration. APPROACH: We used electroencephalography (EEG) data recorded from 20 participants (10 AD patients and 10 healthy controls) during resting state. Functional brain networks were reconstructed using EEG source connectivity computed in different frequency bands. Graph theoretical analyses were performed assess differences between both groups. MAIN RESULTS: Results revealed that AD networks, compared to networks of age-matched healthy controls, are characterized by lower global information processing (integration) and higher local information processing (segregation). Results showed also significant correlation between the alterations in the AD patients' functional brain networks and their cognitive scores. SIGNIFICANCE: These findings may contribute to the development of EEG network-based test that could strengthen results obtained from currently-used neurophysiological tests in neurodegenerative diseases.

Peritubular cells are the site of erythropoietin synthesis in the murine hypoxic kidney.

Erythropoietin (Epo)-producing cells were identified in the murine hypoxic kidney by in situ hybridization. Profound anemia was induced in order to greatly increase Epo production. This resulted in high levels of Epo mRNA in the kidney. 35S-labeled DNA fragments of the murine Epo gene were used as probes for in situ hybridization. Control experiments conducted in parallel included kidneys of nonanemic mice, RNase-treated hypoxic kidney sections, and 35S-labeled non-Epo-related DNA. The Epo probe gave a specific hybridization signal in the hypoxic kidney in the cortex and to a lesser extent in the outer medulla. Glomerular and tubular cells were not labeled. All positive cells were identified as peritubular cells. Using immunofluorescence, we showed that cells with the same topography contained Factor VIII-related antigen. These data demonstrated that peritubular cells, most likely endothelial cells, constitute the major site of Epo production in the murine hypoxic kidney.

Spi-1/PU.1 transgenic mice develop multistep erythroleukemias.

Insertional mutagenesis of the spi-1 gene is associated with the emergence of malignant proerythroblasts during Friend virus-induced acute erythroleukemia. To determine the role of spi-1/PU.1 in the genesis of leukemia, we generated spi-1 transgenic mice. In one founder line the transgene was overexpressed as an unexpected-size transcript in various mouse tissues. Homozygous transgenic animals gave rise to live-born offspring, but 50% of the animals developed a multistep erythroleukemia within 1.5 to 6 months of birth whereas the remainder survived without evidence of disease. At the onset of the disease, mice became severely anemic. Their hematopoietic tissues were massively invaded with nontumorigenic proerythroblasts that express a high level of Spi-1 protein. These transgenic proerythroblasts are partially blocked in differentiation and strictly dependent on erythropoietin for their proliferation both in vivo and in vitro. A complete but transient regression of the disease was observed after erythrocyte transfusion, suggesting that the constitutive expression of spi-1 is related to the block of the differentiation of erythroid precursors. At relapse, erythropoietin-independent malignant proerythroblasts arose. Growth factor autonomy could be partially explained by the autocrine secretion of erythropoietin; however, other genetic events appear to be necessary to confer the full malignant phenotype. These results reveal that overexpression of spi-1 is essential for malignant erythropoiesis and does not alter other hematopoietic lineages.

The dynamic functional core network of the human brain at rest.

The human brain is an inherently complex and dynamic system. Even at rest, functional brain networks dynamically reconfigure in a well-organized way to warrant an efficient communication between brain regions. However, a precise characterization of this reconfiguration at very fast time-scale (hundreds of millisecond) during rest remains elusive. In this study, we used dense electroencephalography data recorded during task-free paradigm to track the fast temporal dynamics of spontaneous brain networks. Results obtained from network-based analysis methods revealed the existence of a functional dynamic core network formed of a set of key brain regions that ensure segregation and integration functions. Brain regions within this functional core share high betweenness centrality, strength and vulnerability (high impact on the network global efficiency) and low clustering coefficient. These regions are mainly located in the cingulate and the medial frontal cortex. In particular, most of the identified hubs were found to belong to the Default Mode Network. Results also revealed that the same central regions may dynamically alternate and play the role of either provincial (local) or connector (global) hubs.

Functional connectivity disruptions correlate with cognitive phenotypes in Parkinson's disease.

Cognitive deficits in Parkinson's disease are thought to be related to altered functional brain connectivity. To date, cognitive-related changes in Parkinson's disease have never been explored with dense-EEG with the aim of establishing a relationship between the degree of cognitive impairment, on the one hand, and alterations in the functional connectivity of brain networks, on the other hand. This study was aimed at identifying altered brain networks associated with cognitive phenotypes in Parkinson's disease using dense-EEG data recorded during rest with eyes closed. Three groups of Parkinson's disease patients (N = 124) with different cognitive phenotypes coming from a data-driven cluster analysis, were studied: G1) cognitively intact patients (63), G2) patients with mild cognitive deficits (46) and G3) patients with severe cognitive deficits (15). Functional brain networks were identified using a dense-EEG source connectivity method. Pairwise functional connectivity was computed for 68 brain regions in different EEG frequency bands. Network statistics were assessed at both global (network topology) and local (inter-regional connections) level. Results revealed progressive disruptions in functional connectivity between the three patient groups, typically in the alpha band. Differences between G1 and G2 (p < 0.001, corrected using permutation test) were mainly frontotemporal alterations. A statistically significant correlation (ρ = 0.49, p < 0.001) was also obtained between a proposed network-based index and the patients' cognitive score. Global properties of network topology in patients were relatively intact. These findings indicate that functional connectivity decreases with the worsening of cognitive performance and loss of frontotemporal connectivity may be a promising neuromarker of cognitive impairment in Parkinson's disease.

Automatic lateralization of temporal lobe epilepsy based on scalp EEG.

OBJECTIVE: The objective of this work is the determination of the lateralization of the epileptic seizure onset zone using the scalp EEG signal processing. METHODS: A comprehensive method based on the evaluation of the evolution of the correlation coefficients computed between bipolar channels (longitudinal montage) was applied to 43 patients (87 seizures). The correlation coefficients are estimated by a nonlinear regression analysis. The methodology that leads to the lateralization is based on several processing steps: segmentation, seizure onset determination and then lateralization. RESULTS: Results show that the mean level of the nonlinear correlation values computed between EEG channels at the seizure onset time is significantly higher on the side of the beginning of a seizure. CONCLUSIONS: The side of the seizure onset was determined for about 80-90% of the seizures studied with a satisfactory high reproducibility level. SIGNIFICANCE: Comparison of nonlinear correlation coefficients between both sides of the brain leads to the determination of the side of seizure onset.

Target cell of the polycythemia-inducing Friend virus: studies with myleran.

A single injection of Myleran reduced the pluripotent hematopoietic stem cell, i.e., colony forming unit(s) (CFU), and the erythropoietin-responsive cell (ERC) in polycythemic mice to around 0.5% that of the controls. Repeated injections of erythropoietin (EP) restored ERC populations, whereas the CFU remained at very low levels. This selective action of Myleran and EP in polycythemic mice seemed to be a good approach for the study of oncogenic action of Friend virus on target cells. When the CFU and ERC compartments were decreased, practically no response to the virus was obtained. When there was an appreciable ERC population present with decreased CFU, leukemogenesis still occurred (as judged by the increased spleen weight). This result was in proportion to the dose of EP, i.e., stimulation of the ERC or closely related cells.

Detection of tumorigenic cells in Friend virus-infected mice: an in vivo methodological investigation.

Tumor formation by subcutaneous transplants of spleens from erythroleukemic mice infected with Friend virus complex inducing polycythemia (FLV-P) is successful only during the late phase of the disease. To determine whether this observation is due to the absence of tumorigenic cells in the early phase of such leukemia or to the inability of standard procedures to detect these cells, the sensitivities of different routes of inoculation in sublethally irradiated or unirradiated syngeneic recipients were examined. The omentum of the sublethally irradiated mouse was found to be a suitable site for the homing and proliferation of recently isolated tumorigenic cells from FLV-P-infected mice, since it proved 1,000 times more sensitive than the usual subcutaneous sites in unirradiated mice. When this sensitive graft in the omentum was applied to detection of tumorigenic cells in the spleens of FLV-P-infected mice, the mean detection time was 20 days after virus infection, compared to 36 days with the usual subcutaneous graft method.