Neural Networks Associated with the Speed-Accuracy Tradeoff: Evidence from the Response Signal Method

This functional neuroimaging (fMRI) study examined the neural networks (spatial patterns of covarying neural activity) associated with the speed-accuracy tradeoff (SAT) in younger adults. The response signal method was used to systematically increase probe duration (125, 250, 500, 1000 and 2000 ms) in a nonverbal delayed-item recognition task. A covariance-based multivariate approach identified three networks that varied with probe duration--indicating that the SAT is driven by three distributed neural networks

Apport de nouvelles techniques dans l’évaluation de patients candidats à une chirurgie d’épilepsie : résonance magnétique à haut champ, spectroscopie proche infrarouge et magnétoencéphalographie

L'épilepsie constitue le désordre neurologique le plus fréquent après les maladies cérébrovasculaires. Bien que le contrôle des crises se fasse généralement au moyen d'anticonvulsivants, environ 30 % des patients y sont réfractaires. Pour ceux-ci, la chirurgie de l'épilepsie s'avère une option intéressante, surtout si l’imagerie par résonance magnétique (IRM) cérébrale révèle une lésion épileptogène bien délimitée. Malheureusement, près du quart des épilepsies partielles réfractaires sont dites « non lésionnelles ». Chez ces patients avec une IRM négative, la délimitation de la zone épileptogène doit alors reposer sur la mise en commun des données cliniques, électrophysiologiques (EEG de surface ou intracrânien) et fonctionnelles (tomographie à émission monophotonique ou de positrons). La faible résolution spatiale et/ou temporelle de ces outils de localisation se traduit par un taux de succès chirurgical décevant. Dans le cadre de cette thèse, nous avons exploré le potentiel de trois nouvelles techniques pouvant améliorer la localisation du foyer épileptique chez les patients avec épilepsie focale réfractaire considérés candidats potentiels à une chirurgie d’épilepsie : l’IRM à haut champ, la spectroscopie proche infrarouge (SPIR) et la magnétoencéphalographie (MEG). Dans une première étude, nous avons évalué si l’IRM de haut champ à 3 Tesla (T), présentant théoriquement un rapport signal sur bruit plus élevé que l’IRM conventionnelle à 1,5 T, pouvait permettre la détection des lésions épileptogènes subtiles qui auraient été manquées par cette dernière. Malheureusement, l’IRM 3 T n’a permis de détecter qu’un faible nombre de lésions épileptogènes supplémentaires (5,6 %) d’où la nécessité d’explorer d’autres techniques. Dans les seconde et troisième études, nous avons examiné le potentiel de la SPIR pour localiser le foyer épileptique en analysant le comportement hémodynamique au cours de crises temporales et frontales. Ces études ont montré que les crises sont associées à une augmentation significative de l’hémoglobine oxygénée (HbO) et l’hémoglobine totale au niveau de la région épileptique. Bien qu’une activation contralatérale en image miroir puisse être observée sur la majorité des crises, la latéralisation du foyer était possible dans la plupart des cas. Une augmentation surprenante de l’hémoglobine désoxygénée a parfois pu être observée suggérant qu’une hypoxie puisse survenir même lors de courtes crises focales. Dans la quatrième et dernière étude, nous avons évalué l’apport de la MEG dans l’évaluation des patients avec épilepsie focale réfractaire considérés candidats potentiels à une chirurgie. Il s’est avéré que les localisations de sources des pointes épileptiques interictales par la MEG ont eu un impact majeur sur le plan de traitement chez plus des deux tiers des sujets ainsi que sur le devenir postchirurgical au niveau du contrôle des crises.Epilepsy is the most common chronic neurological disorder after stroke. The major form of treatment is long-term drug therapy to which approximately 30% of patients are unfortunately refractory to. Brain surgery is recommended when medication fails, especially if magnetic resonance imaging (MRI) can identify a well-defined epileptogenic lesion. Unfortunately, close to a quarter of patients have nonlesional refractory focal epilepsy. For these MRI-negative cases, identification of the epileptogenic zone rely heavily on remaining tools: clinical history, video-electroencephalography (EEG) monitoring, ictal single-photon emission computed tomography (SPECT), and a positron emission tomography (PET). Unfortunately, the limited spatial and/or temporal resolution of these localization techniques translates into poor surgical outcome rates. In this thesis, we explore three relatively novel techniques to improve the localization of the epileptic focus for patients with drug-resistant focal epilepsy who are potential candidates for epilepsy surgery: high-field 3 Tesla (T) MRI, near-infrared spectroscopy (NIRS) and magnetoencephalography (MEG). In the first study, we evaluated if high-field 3T MRI, providing a higher signal to noise ratio, could help detect subtle epileptogenic lesions missed by conventional 1.5T MRIs. Unfortunately, we show that the former was able to detect an epileptogenic lesion in only 5.6% of cases of 1.5T MRI-negative epileptic patients, emphasizing the need for additional techniques. In the second and third studies, we evaluated the potential of NIRS in localizing the epileptic focus by analyzing the hemodynamic behavior of temporal and frontal lobe seizures respectively. We show that focal seizures are associated with significant increases in oxygenated haemoglobin (HbO) and total haemoglobin (HbT) over the epileptic area. While a contralateral mirror-like activation was seen in the majority of seizures, lateralization of the epileptic focus was possible most of the time. In addition, an unexpected increase in deoxygenated haemoglobin (HbR) was noted in some seizures, suggesting possible hypoxia even during relatively brief focal seizures. In the fourth and last study, the utility of MEG in the evaluation of nonlesional drug-refractory focal epileptic patients was studied. It was found that MEG source localization of interictal epileptic spikes had an impact both on patient management for over two thirds of patients and their surgical outcome

Utilizing anatomical information for signal detection in functional magnetic resonance imaging

We are considering the statistical analysis of functional magnetic resonance imaging (fMRI) data. As demonstrated in previous work, grouping voxels into regions (of interest) and carrying out a multiple test for signal detection on the basis of these regions typically leads to a higher sensitivity when compared with voxel-wise multiple testing approaches. In the case of a multi-subject study, we propose to define the regions for each subject separately based on their individual brain anatomy, represented, e.g., by so-called Aparc labels. The aggregation of the subject-specific evidence for the presence of signals in the different regions is then performed by means of a combination function for p-values. We apply the proposed methodology to real fMRI data and demonstrate that our approach can perform comparably to a two-stage approach for which two independent experiments are needed, one for defining the regions and one for actual signal detection

Extending Local Canonical Correlation Analysis to Handle General Linear Contrasts for fMRI Data

Local canonical correlation analysis (CCA) is a multivariate method that has been proposed to more accurately determine activation patterns in fMRI data. In its conventional formulation, CCA has several drawbacks that limit its usefulness in fMRI. A major drawback is that, unlike the general linear model (GLM), a test of general linear contrasts of the temporal regressors has not been incorporated into the CCA formalism. To overcome this drawback, a novel directional test statistic was derived using the equivalence of multivariate multiple regression (MVMR) and CCA. This extension will allow CCA to be used for inference of general linear contrasts in more complicated fMRI designs without reparameterization of the design matrix and without reestimating the CCA solutions for each particular contrast of interest. With the proper constraints on the spatial coefficients of CCA, this test statistic can yield a more powerful test on the inference of evoked brain regional activations from noisy fMRI data than the conventional t-test in the GLM. The quantitative results from simulated and pseudoreal data and activation maps from fMRI data were used to demonstrate the advantage of this novel test statistic

Detection of Motor Changes in Huntington's Disease Using Dynamic Causal Modeling

Neurological Motor Disorder

Representational organization of novel task sets during proactive encoding

Recent multivariate analyses of brain data have boosted our understanding of the organizational principles that shape neural coding. However, most of this progress has focused on perceptual visual regions (Connolly et al., 2012), whereas far less is known about the organization of more abstract, action-oriented representations. In this study, we focused on humans{\textquoteright} remarkable ability to turn novel instructions into actions. While previous research shows that instruction encoding is tightly linked to proactive activations in fronto-parietal brain regions, little is known about the structure that orchestrates such anticipatory representation. We collected fMRI data while participants (both males and females) followed novel complex verbal rules that varied across control-related variables (integrating within/across stimuli dimensions, response complexity, target category) and reward expectations. Using Representational Similarity Analysis (Kriegeskorte et al., 2008) we explored where in the brain these variables explained the organization of novel task encoding, and whether motivation modulated these representational spaces. Instruction representations in the lateral prefrontal cortex were structured by the three control-related variables, while intraparietal sulcus encoded response complexity and the fusiform gyrus and precuneus organized its activity according to the relevant stimulus category. Reward exerted a general effect, increasing the representational similarity among different instructions, which was robustly correlated with behavioral improvements. Overall, our results highlight the flexibility of proactive task encoding, governed by distinct representational organizations in specific brain regions. They also stress the variability of motivation-control interactions, which appear to be highly dependent on task attributes such as complexity or novelty.SIGNIFICANCE STATEMENTIn comparison with other primates, humans display a remarkable success in novel task contexts thanks to our ability to transform instructions into effective actions. This skill is associated with proactive task-set reconfigurations in fronto-parietal cortices. It remains yet unknown, however, how the brain encodes in anticipation the flexible, rich repertoire of novel tasks that we can achieve. Here we explored cognitive control and motivation-related variables that might orchestrate the representational space for novel instructions. Our results showed that different dimensions become relevant for task prospective encoding depending on the brain region, and that the lateral prefrontal cortex simultaneously organized task representations following different control-related variables. Motivation exerted a general modulation upon this process, diminishing rather than increasing distances among instruction representations

Flexible multivariate hemodynamics fMRI data analyses and simulations with PyHRF

International audienceAs part of fMRI data analysis, the pyhrf package provides a set of tools for addressing the two 3 main issues involved in intra-subject fMRI data analysis: (i) the localization of cerebral regions 4 that elicit evoked activity and (ii) the estimation of the activation dynamics also referenced to 5 as the recovery of the Hemodynamic Response Function (HRF). To tackle these two problems, 6 pyhrf implements the Joint Detection-Estimation framework (JDE) which recovers parcel-level 7 HRFs and embeds an adaptive spatio-temporal regularization scheme of activation maps. With 8 respect to the sole detection issue (i), the classical voxelwise GLM procedure is also available 9 through nipy, whereas Finite Impulse Response (FIR) and temporally regularized FIR models 10 are implemented to deal with HRF estimation concerns (ii). Several parcellation tools are also 11 integrated such as spatial and functional clustering. Parcellations may be used for spatial 12 averaging prior to FIR/RFIR analysis or to specify the spatial support of the HRF estimates 13 in the JDE approach. These analysis procedures can be applied either to volumic data sets or 14 to data projected onto the cortical surface. For validation purpose, this package is shipped with 15 artificial and real fMRI data sets, which are used in this paper to compare the outcome of the 16 different available approaches. The artificial fMRI data generator is also described to illustrate 17 how to simulate different activation configurations, HRF shapes or nuisance components. To 18 cope with the high computational needs for inference, pyhrf handles distributing computing 19 by exploiting cluster units as well as multiple cores computers. Finally, a dedicated viewer is 20 presented, which handles n-dimensional images and provides suitable features to explore whole 21 brain hemodynamics (time series, maps, ROI mask overlay)

Detection of motor changes in huntington's disease using dynamic causal modeling

Deficits in motor functioning are one of the hallmarks of Huntington's disease (HD), a genetically caused neurodegenerative disorder. We applied functional magnetic resonance imaging (fMRI) and dynamic causal modeling (DCM) to assess changes that occur with disease progression in the neural circuitry of key areas associated with executive and cognitive aspects of motor control. Seventy-seven healthy controls, 62 pre-symptomatic HD gene carriers (preHD), and 16 patients with manifest HD symptoms (earlyHD) performed a motor finger-tapping fMRI task with systematically varying speed and complexity. DCM was used to assess the causal interactions among seven pre-defined regions of interest, comprising primary motor cortex, supplementary motor area (SMA), dorsal premotor cortex, and superior parietal cortex. To capture heterogeneity among HD gene carriers, DCM parameters were entered into a hierarchical cluster analysis using Ward's method and squared Euclidian distance as a measure of similarity. After applying Bonferroni correction for the number of tests, DCM analysis revealed a group difference that was not present in the conventional fMRI analysis. We found an inhibitory effect of complexity on the connection from parietal to premotor areas in preHD, which became excitatory in earlyHD and correlated with putamen atrophy. While speed of finger movements did not modulate the connection from caudal to pre-SMA in controls and preHD, this connection became strongly negative in earlyHD. This second effect did not survive correction for multiple comparisons. Hierarchical clustering separated the gene mutation carriers into three clusters that also differed significantly between these two connections and thereby confirmed their relevance. DCM proved useful in identifying group differences that would have remained undetected by standard analyses and may aid in the investigation of between-subject heterogeneity

Misconceptions in the use of the General Linear Model applied to functional MRI:a tutorial for junior neuro-imagers

This tutorial presents several misconceptions related to the use the General Linear Model (GLM) in functional Magnetic Resonance Imaging (fMRI). The goal is not to present mathematical proofs but to educate using examples and computer code (in Matlab). In particular, I address issues related to (i) model parameterization (modelling baseline or null events) and scaling of the design matrix; (ii) hemodynamic modelling using basis functions, and (iii) computing percentage signal change. Using a simple controlled block design and an alternating block design, I first show why 'baseline' should not be modelled (model over-parameterization), and how this affects effect sizes. I also show that, depending on what is tested; over-parameterization does not necessarily impact upon statistical results. Next, using a simple periodic vs. random event related design, I show how the haemodynamic model (haemodynamic function only or using derivatives) can affects parameter estimates, as well as detail the role of orthogonalization. I then relate the above results to the computation of percentage signal change. Finally, I discuss how these issues affect group analysis and give some recommendations