Tesis sobre el tema "Spike train, data-analysis, SPIKE-distance"

This thesis proposes a novel approach for functional connectivity studies of neuronal signal recordings based on statistical signal processing analysis in the frequency domain using Multivariate Partial Coherence (MVPC) combined with network theory measures. MVPC is applied to spike trains signals to make inferences about the underlying network structure. The presence of connections between single unit spike trains is estimated using both coherence and MVPC analysis. Scalability of MVPC analysis is investigated through application to simulated spike train data with up to 100 simultaneous spike trains generated from a network of excitatory and inhibitory cortical neurons. Stable MVPC estimates were obtained with up to 198 predictors in partial coherence estimates, using a combination of simulated cortical neuron data and additional Poisson spike train predictors. MVPC provides higher order partial coherence analysis for multi-channel spike trains signals, removing effects of common influences in pairwise connectivity estimates. Network measures applied to binary and weighted adjacency measures derived from coherence and partial coherence are compared to determine the differences in unconditional and conditional networks of spike train interactions. A combination of MVPC analysis along with network theory analysis provides a systematic approach for multi-channel spike train signals. The proposed method is applied to simulated and multi-electrode array (MEA) spike train data. The MEA data consists of 19 single unit channels recorded from a study of connectivity in a model of kainic acid (KA) induced epileptiform activity for mesial temporal lobe epilepsy (mTLE) in a rat. The network theory analysis uses basic measures on both conditional and unconditional network, which highlights the differences in network structure and characteristics between the two representations. Complex analysis on conditional networks is useful in describing the properties of integration and segregation in the network.

We study two methods of data analysis which are common tools for the analysis of neuronal data. In particular, we examine how causal interactions between brain regions can be investigated using time series reflecting the neural activity in these regions. Furthermore, we analyze a method used to study the neural code that evaluates the discrimination of the responses of single neurons elicited by different stimuli. This discrimination analysis is based on the quantification of the similarity of the spike trains with time scale parametric spike train distances. In each case we describe the methods used for the analysis of the neuronal data and we characterize their specificity using simulated or exemplary experimental data. Taking into account our results, we comment the previous studies in which the methods have been applied. In particular, we focus on the interpretation of the statistical measures in terms of underlying neuronal causal connectivity and properties of the neural code, respectively.
Estudiem dos mètodes d'anàlisi de dades que són eines habituals per a l'anàlisi de dades neuronals. Concretament, examinem la manera en què les interaccions causals entre regions del cervell poden ser investigades a partir de sèries temporals que reflecteixen l'activitat neuronal d'aquestes regions. A més a més, analitzem un mètode emprat per estudiar el codi neuronal que avalua la discriminació de les respostes de neurones individuals provocades per diferents estímuls. Aquesta anàlisi de la discriminació es basa en la quantificació de la similitud de les seqüències de potencials d'acció amb distàncies amb un paràmetre d'escala temporal. Tenint en compte els nostres resultats, comentem els estudis previs en els quals aquests mètodes han estat aplicats. Concretament, ens centrem en la interpretació de les mesures estadístiques en termes de connectivitat causal neuronal subjacent i propietats del codi neuronal, respectivament.

The interaction between neuronal cells can be identified as the computing mechanism of the brain. Neurons are complex cells that do not operate in isolation, but they are organized in a highly connected network structure. There is experimental evidence that groups of neurons dynamically synchronize their activity and process brain functions at all levels of complexity. A fundamental step to prove this hypothesis is to analyze large sets of single neurons recorded in parallel. Techniques to obtain these data are meanwhile available, but advancements are needed in the pre-processing of the large volumes of acquired data and in data analysis techniques. Major issues include extracting the signal of single neurons from the noisy recordings (referred to as spike sorting) and assessing the significance of the synchrony. This dissertation addresses these issues with two complementary strategies, both founded on the manipulation of point processes under rigorous analytical control. On the one hand I modeled the effect of spike sorting errors on correlated spike trains by corrupting them with realistic failures, and studied the corresponding impact on correlation analysis. The results show that correlations between multiple parallel spike trains are severely affected by spike sorting, especially by erroneously missing spikes. When this happens sorting strategies characterized by classifying only good'' spikes (conservative strategies) lead to less accurate results than tolerant'' strategies. On the other hand, I investigated the effectiveness of methods for assessing significance that create surrogate data by displacing spikes around their original position (referred to as dithering). I provide analytical expressions of the probability of coincidence detection after dithering. The effectiveness of spike dithering in creating surrogate data strongly depends on the dithering method and on the method of counting coincidences. Closed-form expressions and bounds are derived for the case where the dither equals the allowed coincidence interval. This work provides new insights into the methodologies of identifying synchrony in large-scale neuronal recordings, and of assessing its significance.
Die Informationsverarbeitung im Gehirn erfolgt maßgeblich durch interaktive Prozesse von Nervenzellen, sogenannten Neuronen. Diese zeigen eine komplexe Dynamik ihrer chemischen und elektrischen Eigenschaften. Es gibt deutliche Hinweise darauf, dass Gruppen synchronisierter Neurone letztlich die Funktionsweise des Gehirns auf allen Ebenen erklären können. Um die schwierige Frage nach der genauen Funktionsweise des Gehirns zu beantworten, ist es daher notwendig, die Aktivität vieler Neuronen gleichzeitig zu messen. Die technischen Voraussetzungen hierfür sind in den letzten Jahrzehnten durch Multielektrodensyteme geschaffen worden, die heute eine breite Anwendung finden. Sie ermöglichen die simultane extrazelluläre Ableitung von bis zu mehreren hunderten Kanälen. Die Voraussetzung für die Korrelationsanalyse von vielen parallelen Messungen ist zunächst die korrekte Erkennung und Zuordnung der Aktionspotentiale einzelner Neurone, ein Verfahren, das als Spikesortierung bezeichnet wird. Eine weitere Herausforderung ist die statistisch korrekte Bewertung von empirisch beobachteten Korrelationen. Mit dieser Dissertationsschrift lege ich eine theoretische Arbeit vor, die sich der Vorverarbeitung der Daten durch Spikesortierung und ihrem Einfluss auf die Genauigkeit der statistischen Auswertungsverfahren, sowie der Effektivität zur Erstellung von Surrogatdaten für die statistische Signifikanzabschätzung auf Korrelationen widmet. Ich verwende zwei komplementäre Strategien, die beide auf der analytischen Berechnung von Punktprozessmanipulationen basieren. In einer ausführlichen Studie habe ich den Effekt von Spikesortierung in mit realistischen Fehlern behafteten korrelierten Spikefolgen modeliert. Zum Vergleich der Ergebnisse zweier unterschiedlicher Methoden zur Korrelationsanalyse auf den gestörten, sowie auf den ungestörten Prozessen, leite ich die entsprechenden analytischen Formeln her. Meine Ergebnisse zeigen, dass koinzidente Aktivitätsmuster multipler Spikefolgen durch Spikeklassifikation erheblich beeinflusst werden. Das ist der Fall, wenn Neuronen nur fälschlicherweise Spikes zugeordnet werden, obwohl diese anderen Neuronen zugehörig sind oder Rauschartefakte sind (falsch positive Fehler). Jedoch haben falsch-negative Fehler (fälschlicherweise nicht-klassifizierte oder missklassifizierte Spikes) einen weitaus grösseren Einfluss auf die Signifikanz der Korrelationen. In einer weiteren Studie untersuche ich die Effektivität einer Klasse von Surrogatmethoden, sogenannte Ditheringverfahren, welche paarweise Korrelationen zerstören, in dem sie koinzidente Spikes von ihrer ursprünglichen Position in einem kleinen Zeitfenster verrücken. Es zeigt sich, dass die Effektivität von Spike-Dithering zur Erzeugung von Surrogatdaten sowohl von der Dithermethode als auch von der Methode zur Koinzidenzzählung abhängt. Für die Wahrscheinlichkeit der Koinzidenzerkennung nach dem Dithern stelle ich analytische Formeln zur Verfügung. Die vorliegende Arbeit bietet neue Einblicke in die Methoden zur Korrelationsanalyse auf multi-variaten Punktprozessen mit einer genauen Untersuchung von unterschiedlichen statistischen Einflüssen auf die Signifikanzabschätzung. Für die praktische Anwendung ergeben sich Leitlinien für den Umgang mit Daten zur Synchronizitätsanalyse.

Neuroscientists are increasingly overwhelmed by new recordings of the nervous system. These recordings are significantly increasing in size due to new electrophysiological techniques, such as multi-electrode arrays. These techniques can simultaneously record the electrical activity (or spike trains) from thousands of neurons. These new datasets are larger than the traditional datasets recorded from single electrodes where fewer than ten spike trains are usually recorded. Consequently, new tools are now required to effectively analyse these new datasets. This thesis describes how techniques from the field of Visual Analytics can be applied to detect specific patterns in spike train data. These techniques are realised in a software tool called Neurigma. Neurigma is a collection of visual representations of spike train data that are unified to provide a coordinated representation of the data. The visual representations within Neurigma include: an interactive raster plot, an improved correlation grid, a novel representation called the correlation plot (which includes a novel coupling estimation algorithm), and a novel network diagram. These views provide insight into spike train data, and particularly, they identify correlated patterns, called functional connectivity. Within this thesis Neurigma is used to analyse synthetically generated datasets and experimental recordings. Three main findings are presented. First, propagating spiral patterns are identified within recordings from the neonatal mouse retina. Second, functional connectivity is identified within the cat visual cortex. Finally, the functional connectivity of a large synthetic dataset, of 1000 spike trains, is accurately classified into direct, indirect and common input coupling.

An important problem in neuroscience is the assessment of the connectivity between neurons from their spike trains. One recent approach developed for the detection of directional couplings between dynamics based on recorded point processes is the nonlinear interdependence measure L. In this thesis we first use the Hindmarsh-Rose model system to test L in the presence of noise and for different spiking regimes of the dynamics. We then compare the performance of L against the linear cross-correlogram and two spike train distances. Finally, we apply all measures to neuronal spiking data from an intracranial whole-night recording of a patient with epilepsy. When applied to simulated data, L proves to be versatile, robust and more sensitive than the linear measures. Instead, in the real data the linear measures find more connections than L, in particular for neurons in the same brain region and during slow wave sleep.
Un problema important en la neurociència és determinar la connexió entre neurones utilitzant dades dels seus trens d’impulsos. Un mètode recent que afronta la detecció de connexions direccionals entre dinàmiques utilitzant processos puntuals és la mesura d’interdependència no lineal L. En aquesta tesi, utilitzem el model de Hindmarsh-Rose per testejar L en presència de soroll i per diferents règims dinàmics. Després comparem el desempenyorament de L en comparació al correlograma lineal i a dues mesures de trens d’impulsos. Finalment, apliquem totes aquestes mesures a dades d’impulsos de neurones obtingudes de senyals intracranials electroencefalogràfiques gravades durant una nit a un pacient amb epilèpsia. Quan utilitzem dades simulades, L demostra que és versàtil, robusta i més sensible que les mesures lineals. En canvi, utilitzant dades reals, les mesures lineals troben més connexions que L, especialment entre neurones en la mateixa àrea del cervell i durant la fase de son d’ones lentes.

A "complex" system typically has a relatively large number of dynamically interacting components and tends to exhibit emergent behavior that cannot be explained by analyzing each component separately. A biological neural network is one example of such a system. A multi-agent model of such a network is developed to study the relationships between a network's structure and its spike train output. Using this model, inferences are made about the synaptic structure of networks through cluster analysis of spike train summary statistics A complexity measure for the network structure is also presented which has a one-to-one correspondence with the standard time series complexity measure sample entropy.

Recently-developed technologies for monitoring activity in populations of neurons make it possible for the first time, in principle, to ask many basic questions in neuroscience. However, computational tools for analyzing newly available data need to be developed. The goal of this thesis is to contribute to this effort by focusing on two specific problems. First, we used a point-process regression framework to provide a methodology for statistical assessment of the link between neural spike synchrony and network-wide oscillations. In simulations, we showed that our method can recover ground-truth relationships, and in two types of spike train data we illustrated the kinds of results the method can produce. The approach improves on methods in the literature and may be adapted to many different experimental settings. Second, we considered the problem of source extraction in calcium imaging data, i.e., the detection of neurons within a field of view and the extraction of each neuron’s activity. The data we mainly focus on are recorded with a microendoscope, which has the unique advantage of imaging deep brain regions in freely behaving animals. These data suffer from high levels of background fluorescence, as well as the potential for overlapping neuronal signals. Based on the existing constrained nonnegative matrix factorization (CNMF) framework, we developed an efficient method to process microendoscopic data. Our method utilizes a novel algorithm to initialize the spatial shapes and temporal activity of the neurons from the raw video data independently from the strong fluctuating background. This step ensures the efficiency and accuracy of solving a nonconvex CNMF problem. Our method also models the complicated background by including its low-spatial frequency structure and the locally-low-rank feature to avoid absorbing cellular signals into the background term. We developed a tractable solution to estimate the background activity using this new model. After subtracting the approximated background, we followed the CNMF framework to demix neural signals and recover denoised and deconvolved temporal activity. We optimized several algorithms in solving the CNMF problems to get accurate results. In practice, our method outperforms all existing methods and has been adopted by many experimental labs.

This thesis describes the research underpinning and the development of a cross platform application for the analysis of simultaneously recorded multi-dimensional spike trains. These spike trains are believed to carry the neural code that encodes information in a biological brain. A number of statistical methods already exist to analyse the temporal relationships between the spike trains. Historically, hundreds of spike trains have been simultaneously recorded, however as a result of technological advances recording capability has increased. The analysis of thousands of simultaneously recorded spike trains is now a requirement. Effective analysis of large data sets requires software tools that fully exploit the capabilities of modern research computers and effectively manage and present large quantities of data. To be effective such software tools must; be targeted at the field under study, be engineered to exploit the full compute power of research computers and prevent information overload of the researcher despite presenting a large and complex data set. The Visualisation Studio application produced in this thesis brings together the fields of neuroscience, software engineering and information visualisation to produce a software tool that meets these criteria. A visual programming language for neuroscience is produced that allows for extensive pre-processing of spike train data prior to visualisation. The computational challenges of analysing thousands of spike trains are addressed using parallel processing to fully exploit the modern researcher’s computer hardware. In the case of the computationally intensive pairwise cross-correlation analysis the option to use a high performance compute cluster (HPC) is seamlessly provided. Finally the principles of information visualisation are applied to key visualisations in neuroscience so that the researcher can effectively manage and visually explore the resulting data sets. The final visualisations can typically represent data sets 10 times larger than previously while remaining highly interactive.

The thesis is from the field of data-analysis and computational neuroscience. In the thesis I improved existing spike train distance measures and developed new ones. The main methods used are ISI-distance, SPIKE-distance and SPIKE-Synchronization.

This dissertation introduces new methodologies for the analysis of neural spike trains. Biological properties of the nervous system, and how they are reflected in neural data, can motivate specific analytic tools. Some of these biological aspects motivate multiscale frameworks, which allow for simultaneous modelling of the local and global behaviour of neurons. Chapter 1 provides the preliminary background on the biology of the nervous system and details the concept of information and randomness in the analysis of the neural spike trains. It also provides the reader with a thorough literature review on the current statistical models in the analysis of neural spike trains. The material presented in the next six chapters (2-7) have been the focus of three papers, which have either already been published or are being prepared for publication. It is demonstrated in Chapters 2 and 3 that the multiscale complexity penalized likelihood method, introduced in Kolaczyk and Nowak (2004), is a powerful model in the simultaneous modelling of spike trains with biological properties from different time scales. To detect the periodic spiking activities of neurons, two periodic models from the literature, Bickel et al. (2007, 2008); Shao and Li (2011), were combined and modified in a multiscale penalized likelihood model. The contributions of these chapters are (1) employinh a powerful visualization tool, inter-spike interval (ISI) plot, (2) combining the multiscale method of Kolaczyk and Nowak (2004) with the periodic models ofBickel et al. (2007, 2008) and Shao and Li (2011), to introduce the so-called additive and multiplicative models for the intensity function of neural spike trains and introducing a cross-validation scheme to estimate their tuning parameters, (3) providing the numerical bootstrap confidence bands for the multiscale estimate of the intensity function, and (4) studying the effect of time-scale on the statistical properties of spike counts. Motivated by neural integration phenomena, as well as the adjustments for the neural refractory period, Chapters 4 and 5 study the Skellam process and introduce the Skellam Process with Resetting (SPR). Introducing SPR and its application in the analysis of neural spike trains is one of the major contributions of this dissertation. This stochastic process is biologically plausible, and unlike the Poisson process, it does not suffer from limited dependency structure. It also has multivariate generalizations for the simultaneous analysis of multiple spike trains. A computationally efficient recursive algorithm for the estimation of the parameters of SPR is introduced in Chapter 5. Except for the literature review at the beginning of Chapter 4, the rest of the material within these two chapters is original. The specific contributions of Chapters 4 and 5 are (1) introducing the Skellam Process with Resetting as a statistical tool to analyze neural spike trains and studying its properties, including all theorems and lemmas provided in Chapter 4, (2) the two fairly standard definitions of the Skellam process (homogeneous and inhomogeneous) and the proof of their equivalency, (3) deriving the likelihood function based on the observable data (spike trains) and developing a computationally efficient recursive algorithm for parameter estimation, and (4) studying the effect of time scales on the SPR model. The challenging problem of multivariate analysis of the neural spike trains is addressed in Chapter 6. As far as we know, the multivariate models which are available in the literature suffer from limited dependency structures. In particular, modelling negative correlation among spike trains is a challenging problem. To address this issue, the multivariate Skellam distribution, as well as the multivariate Skellam process, which both have flexible dependency structures, are developed. Chapter 5 also introduces a multivariate version of Skellam Process with Resetting (MSPR), and a so-called profile-moment likelihood estimation of its parameters. This chapter generalizes the results of Chapter 4 and 5, and therefore, except for the brief literature review provided at the beginning of the chapter, the remainder of the material is original work. In particular, the contributions of this chapter are (1) introducing multivariate Skellam distribution, (2) introducing two definitions of the Multivariate Skellam process in both homogeneous and inhomogeneous cases and proving their equivalence, (3) introducing Multivariate Skellam Process with Resetting (MSPR) to simultaneously model spike trains from an ensemble of neurons, and (4) utilizing the so-called profile-moment likelihood method to compute estimates of the parameters of MSPR. The discussion of the developed methodologies as well as the ``next steps'' are outlined in Chapter 7.

In this thesis I consider the cross-correlation analysis of spike train data, in two parts. In the first part, I consider the question of the proper interpretation of peaks in covariograms: It is known that peaks in the covariogram of the spike trains of two cells are due to covariations, not time-locked to the stimulus, in the responses of the two cells. Such peaks, even when they have widths on the order of 10s of milliseconds, are often interpreted as evidence of spike timing coordination between the two cells. However, there are other ways to covary which can generate very similar peaks. I describe two of them here: (1) covariations, over different trials, in the overall latency of the response; and (2) covariations, over different trials, in the overall excitability (i.e. average firing rate) of the response. I show how each of these leads to a peak in the covariogram, and how to distinguish such peaks from each other and from peaks due to spike timing coordination. In particular, I describe how to separate the excitability and spike timing components of a covariogram when both types of correlations are present. The second part of the thesis studies the spike train data obtained in multiple-cell recordings in LGN of cat by Adam Sillito and colleagues (Sillito, Jones, Gerstein and West, 1994, Nature 369(6480):479-482). Analysis of this data, including the use of some of the novel insights and methods described in the first part of the thesis, shows that (1) the observed correlations between pairs of cells can be well described in terms of covariations in the latencies and excitabilities of the two cells; (2) the correlations have a time scale of lOs of seconds (20-100 s); (3) the correlations are not specific to the orientation of the drifting sine wave gratings used to drive the geniculate cells under study. A computational model, based on Huguenard and McCormick's (1992) model of thalamic cells, shows that covariations in the resting membrane potential of the two covarying cells can lead to covariogram peaks similar to those found in the experimental data. Together with Sillito et al.'s observation that correlations between pairs of cells only occurred when both members of the pair had the same receptive field types (On/Off/X/Y), and the observation that in some of the records, cells not only fail to positively covary but even anti-covary, this suggests that Silltio et al.'s data holds evidence for an intriguing and hitherto undocumented phenomenon: the possibly diffuse, but cell class-specific, control of resting membrane potential in LGN neurons.

The temporal structure of neuronal spike trains in the visual cortex can provide detailed information about the stimulus and about the neuronal implementation of visual processing. Spike trains recorded from the macaque motion area MT in previous studies (Newsome et al., 1989a; Britten et al., 1992; Zohary et al., 1994) are analyzed here in the context of the dynamic random dot stimulus which was used to evoke them. If the stimulus is incoherent, the spike trains can be highly modulated and precisely locked in time to the stimulus. In contrast, the coherent motion stimulus creates little or no temporal modulation and allows us to study patterns in the spike train that may be intrinsic to the cortical circuitry in area MT. Long gaps in the spike train evoked by the preferred direction motion stimulus are found, and they appear to be symmetrical to bursts in the response to the anti-preferred direction of motion. A novel cross-correlation technique is used to establish that the gaps are correlated between pairs of neurons. Temporal modulation is also found in psychophysical experiments using a modified stimulus. A model is made that can account for the temporal modulation in terms of the computational theory of biological image motion processing. A frequency domain analysis of the stimulus reveals that it contains a repeated power spectrum that may account for psychophysical and electrophysiological observations.

Some neurons tend to fire bursts of action potentials while others avoid burst firing. Using numerical and analytical models of spike trains as Poisson processes with the addition of refractory periods and bursting, we are able to account for peaks in the power spectrum near 40 Hz without assuming the existence of an underlying oscillatory signal. A preliminary examination of the local field potential reveals that stimulus-locked oscillation appears briefly at the beginning of the trial.

Identifying reliable screening tools and characterizing tolerant germplasm sources is essential for developing wheat (Triticum aestivum L.) varieties suited for the hot areas of the world. Our objective was to evaluate heat tolerance of promising wild tetraploid wheat (Triticum turgidum L.) accessions that could be used as sources of heat tolerance in common- and durum-wheat (Triticum durum) breeding programs. We screened 109 wild tetraploid wheat accessions collected by the International Center for Agriculture Research in the Dry Areas (ICARDA) from the hottest wheat growing areas in Africa and Asia, as well as, two common wheat checks for their response to heat stress by measuring damage to the thylakoid membranes, flag leaf temperature depression (FLTD), and spike temperature depression (STD) during exposure to heat stress for 16 beginning at anthesis. Measurements were taken on the day of anthesis then 4, 8, 12, and 16 days post anthesis (DPA) under controlled optimum and heat-stress conditions. Individual kernel weight (IKW) and heat susceptibility index (HSI) measurements were also obtained. Prolonged exposure to heat stress was associated with increased damage to thylakoid membranes, as indicated by the high ratio of constant fluorescence (O) to peak variable fluorescence (P). A positive and significant correlation was found between O/P ratio and both FLTD and STD under heat-stress conditions. A negative and significant correlation was found between FLTD and HSI and between STD and HSI based on the second and third measurements (4 and 8 DPA). Correlations obtained after the third measurement were not significant because heat-stress accelerated maturity and senescence. For a pedigree-based mapping strategy a family approach was then developed by crossing and back-crossing heat-tolerant and heat-susceptible germplasm. A set of 800 lines resulting from the pedigree-based family approach was phenotyped using FLTD, chlorophyll content and yield and its components under heat stress. Genotyping of these lines was accomplished using simple sequence repeat (SSRs) markers. Some QTLs associated with heat stress tolerance were identified. This study identified potential heat-tolerant wild tetraploid wheat germplasm and QTL conditioning heat tolerance that can be incorporated into wheat breeding programs to improve cultivated common and durum wheat.

Fusarium head blight (FHB), caused mainly by Fusarium graminearum (teleomorph: Gibberella zeae), is one of the most damaging diseases of wheat. A ‘Brio’/‘TC 67’ spring wheat population was used to map quantitative trait loci (QTLs) for resistance to FHB, and to study the association of morphological and developmental characteristics with FHB resistance. Interval mapping (IM) detected a major QTL on chromosome 5AL for resistance to disease severity (type II resistance) and Fusarium-damaged kernels (FDK) under greenhouse and field conditions, respectively. Inconsistent QTL(s) was also detected on chromosome 5BS for disease severity and index using field data. The associations of plant height and number of days to anthesis were negative with disease incidence, severity, index, and deoxynivalenol (DON) accumulation data under field conditions. However, number of days to anthesis was positively correlated with disease severity (greenhouse) and FDK (field). Awnedness had a negative effect on FHB, namely the presence of awns resulted in less disease in the population. Spike threshability also affected FHB so that the hard threshable genotypes represented lower disease. Phylogenetic relationships of putative F. graminearum isolates from different sources were characterized using Tri101 gene sequencing data. Canadian and Iranian isolates clustered in F. graminearum lineage 7 (=F. graminearum sensu stricto) within the F. graminearum clade while the isolates received from CIMMYT, Mexico were placed in F. graminearum lineage 3 (=Fusarium boothii) within the Fg clade or Fusarium cerealis. The PCR assay based on the Tri12 gene revealed the presence of the NIV, 3-ADON, and 15-ADON chemotypes with 15-ADON being the predominant chemotype. While we did not find the NIV chemotype among the Canadian isolates, it was the predominant chemotype among the Iranian isolates. High variation in aggressiveness was observed among and within Fusarium species tested, with the isolates of F. graminearum sensu stricto being the most aggressive and the NIV chemotype being the least aggressive. The interactions between Fusarium isolates and wheat genotypes from different sources were investigated by inoculating isolates of F. graminearum sensu stricto and F. boothii on wheat genotypes. Significant differences were observed among the genotypes inoculated by single isolates. Results also showed significant interactions between Fusarium isolates and wheat genotypes. The F. boothii isolates from CIMMYT produced low disease symptom and infection on wheat genotypes regardless of the origin of the genotypes while F. graminearum sensu stricto isolates from Canada and Iran resulted in higher FHB scores.