Haut PDF Functional Neuroimaging Group Studies

Functional Neuroimaging Group Studies

Functional Neuroimaging Group Studies

Variability of brain shape The variability of brain size and shape is mostly observed through anatomical imaging and various computational geometric proce- dures that measure the thickness, the regularity of the cortical surface, or some of its singularities (sulcal pitts, sulci fundi etc.). The variability of such features readily poses a challenge for the comparison of brains from different subjects: what makes each brain location unique from an anatomical perspective ? Or put differently, how to warp each individual brain such that the localized individual features can be con- sidered as corresponding to each other ? The best approaches so far consist of first compensating for differences in image pose and brain size through linear transfor- mations, then using high-dimensional diffeomorphic registration to align individual gray matter outline approximately [ 1 ], and then to perform statistical analysis, yet in the absence of further guarantee on the identity of the tested structures. This framework results in an uncertainty of about 10mm on the actual voxel correspon- dences, which can be taken as a blur on the results of any group analysis [ 17 , 33 ]. Current attempts to improve upon this situation rely on surface-based mapping [ 8 ] –yet without any formal guarantee of accurately aligning cyto-architectural areas nor functional areas– or using functional localizer experiments to define individual regions of interest [ 22 ].
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Robust Group-Level Inference in Neuroimaging Genetic Studies

Robust Group-Level Inference in Neuroimaging Genetic Studies

Keywords-Robust regression, fMRI, neuroimaging; I. I NTRODUCTION The statistical analysis of neuroimaging data is challeng- ing since they are composed of multiple correlated descrip- tors (the images’ voxels) the number of which is much larger than the number of observations. These data are observed in the presence of a complex structured noise. Subject performance, image acquisition, and data preprocessing are additional sources of variability that furthermore often lead to the presence of outliers into the datasets. These can cause dramatic drops in the performance of analysis methods. As the high-dimensional context prevents manual data screen- ing, some outlier detection methods have to be used to provide a statistical control on subjects inclusion [ 1 ]. Yet, it remains unclear whether or not outliers should be removed, and, if so, what tolerance to choose. Alternatively, several outlier-resistant methods has been proposed for statistical inference in neuroimaging, although they are still not widely used. Beyond outlier-resistance, such robust methods seem better adapted to real world data since they also compensate for inexact hypotheses (e.g. data normality, homogeneous dataset). Wager [ 2 ] first showed that using robust regression (RLM) resulted in sensitivity improvements in group studies as compared to the use of Ordinary Least Squares (OLS) regression. Penny [ 3 ] and Woolrich [ 4 ] separate regular data from outliers with Bayesian mixture models. These studies however involve less than 20 subjects.
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An empirical comparison of surface-based and volume-based group studies in neuroimaging

An empirical comparison of surface-based and volume-based group studies in neuroimaging

It is important to notice that we did not do any systematic study of dierent pipelines, and that part of the results described here would be slightly altered when using dierent pipelines and pre-processing tools, e.g. using only FreeSurfer pipeline, and that constant improvement of these tools also continuously changes the picture. In particular, the three following points should be considered carefully: i) the number of data resamplings performed along the analysis pipeline (three in the present case: the rst one during distortion correction, a second one during motion correction and the third one during the projection onto the surface), ii) the potential mismatch remaining between EPI and T1 data, in particular when the acquisitions do not entirely cover the brain, e.g. if the tip of the motor cortex is not within the acquired volume, as it was the case in some subjects in our dataset and iii) the choice and parametrization of the projection method, which has to trade o sensitivity, e.g. integrating over a larger domain to avoid missing some signal, against specicity, avoiding bleeding across regions or tissues. We defer a more exhaustive study of these dierent processing steps to future work. Still, we believe that the main eects described here would carry on to many surface-based studies. It is hard to predict whether stronger fMRI contrasts would alter the conclusions: on the one hand, the decrease of false negatives may benet surface-based analyses more than volume-based analyses; on the other hand, the sensitivity may saturate.
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Group analysis in functional neuroimaging: selecting subjects using similarity measures.

Group analysis in functional neuroimaging: selecting subjects using similarity measures.

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Neuroanatomical correlates of haptic object processing: combined evidence from tractography and functional neuroimaging

Neuroanatomical correlates of haptic object processing: combined evidence from tractography and functional neuroimaging

8 standard general linear model (GLM) was fitted between different conditions after adjusting for the effects of six head motion parameters obtained from the realignment procedure. Lastly, to yield group results, a random-effect analysis was implemented on individual contrast images (objects vs. scrambled for the visual contrast and objects vs. texture for the haptic contrast) with a one-sample t-test for statistical inference. Here, less stringent thresholds (p < 0.001, uncorrected) were applied to both functional localizer runs. To extract the sufficient amount of white matter streamlines using ROIs located in the grey matter area, the sizes of ROIs had to be large enough to include superficial white matter areas. This method is in line with several studies using uncorrected values from functional ROIs that extract fILF, for example (Gschwind et al, 2012; Gomez et al, 2015). With this threshold, group shape-selective regions were delineated as clusters for both sensory modalities in the following analyses. Since haptic exploration was done using the right (dominant) hand and since previous findings have also implicated only contra-lateral pathways (that is, left ILF, left SLF _ft for visuo-haptic shape processing, see Lee Masson, 2016b), the ipsilateral (right) hemisphere was masked out for the selection of fROIs and we only report relevant activation in the left hemisphere.
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Sedation of Patients With Disorders of Consciousness During Neuroimaging: Effects on Resting State Functional Brain Connectivity.

Sedation of Patients With Disorders of Consciousness During Neuroimaging: Effects on Resting State Functional Brain Connectivity.

strict patient inclusion criteria for this study. These were: diagnosed as being in the VS/UWS or MCS, as assessed with the CRS-R; scanning occurred more than 2 weeks after initial brain injury in stabilized patients; propofol was used as the sole sedative agent (for the sedated group); an absence of large hemorrhage effects, movement artifacts, foreign body artifacts, midline shifts, acquisition artifacts, low gray– white matter contrast, or exceptionally severe structural brain injury, as assessed by careful visual inspection of the T1 images by an expert. Both DOC groups were matched for age at onset, etiology, time spent in DOC, diagnosis, CRS-R total score, and movement intensities, as assessed both parametrically and nonparametrically. The diagno- sis of MCS or VS/UWS was based on behavioral analysis with the CRS-R, which was repeated several times during a week and performed by trained professionals. 37 The CRS-R is a standardized scale that is currently considered to be the most trustworthy behavioral diagnosis tool for patients with DOC available. 9,38 The control group consisted of 20 healthy, unsedated control subjects (mean age = 46 ± 18 years). The study was approved by the Ethics Committee of the Medical School of the University of Liège and the IRB. Written informed consent to participate in the study was obtained from the subjects themselves in the case of healthy controls, and from the legal surrogates of the patients.
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Investigating Human Neurovascular Coupling Using Functional Neuroimaging: A Critical Review of Dynamic Models

Investigating Human Neurovascular Coupling Using Functional Neuroimaging: A Critical Review of Dynamic Models

CONCLUSION Different models including a specific NVC compartment were previously used in human and animal hemodynamic-based studies. However, these models were rarely built for specifically investigating NVC in normal or pathological conditions. While descriptive models appear potentially usable for comparing patients with cerebrovascular disorders from healthy subjects, explanatory models may offer new hypotheses for investigating in vivo some specific aspects of NVC in normal and pathological conditions. Before using these models for exploring NVC in healthy subjects and patients, the following aspects will need however to be considered; (1) the advantage and risk of using fixed parameter values obtained from the literature should be carefully evaluated according to the study aim and underlying pathological condition, (2) the number of variables needed and complexity of dynamic processes should be reduced to the minimum, (3) the linearity or nonlinearity of the underlying mechanisms should be carefully examined, and (4) the validity always discussed for both normal and pathological situations. We recommend that, in first approach, modeling of NVC in human should firstly restrict to the most macroscopic models (FF, BF, and PI; Figure 3). But these models may fail to fit certain dynamic of the blood flow response, for instance, occurring during longer activation or in pathological NVC. In these cases, the need of models with more parameters (D-C) or including nonlinearity may reveal relevant (AC). Finally, the most complex explicative models (3CW and LEVC) of NVC should be reserved to the modeling of microscopic flow signal imaging in animals, like two-photon imaging.
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Neural signature of DCD: a critical review of MRI neuroimaging studies

Neural signature of DCD: a critical review of MRI neuroimaging studies

DCD = DD + DCD = DD Sample: ADHD, attention-deficit hyperactivity disorder; ASD, autism spectrum disorder; CD, conduct disorder; DCD, developmental coordination disorder children; DD, developmental dyslexia; F, females; FASD, fetal alcohol spectrum Disorder; FIQ, full intelligence quotient; ID, intellectual disability; IQ, intelligence quotient; LH, left handed; M, males; ODD, opposite defiant disorder; R, range; RD, reading disabilities; SLI, specific language impairment; SNS, soft neurological signs; TD, typically developing children; VLBW, very low birth weight; Tests: ADOS, autism diagnostic observation schedule; CPRS-R, Conners’ Parent Rating Scale-Revised; DCDQ, developmental coordination disorder questionnaire; DICA-IV, Diagnostic Interview for Children and Adolescents-IV; DSM, diagnostic and statistical manual of mental disorders; FTT, finger-tapping task; KBIT-2, Kaufman Brief Intelligence Test, second ed; MABC, movement assessment battery for children; MABC2-C, checklist MABC-2; ROCF, Rey–Osterrieth complex figure; SRS, Social Responsiveness Scale; SRTT, serial reaction time task; WASI, Wechsler Abbreviated Scale of Intelligence; WISC, Wechsler Intelligence Scale for Children; ZNA, Zurich neuromotor assessment; Brain acquisition and analysis: AD, axial diffusivity; CT, cortical thickness; DTI, diffusion tensor imaging; ET, echo time; FA, fractional anisotropy; FC, functional connectivity; FDR, false discovery rate; fMRI, functional magnetic resonance imaging; fMRI-rs, functional magnetic resonance imaging-resting state; FOV, field of view; FWE, family wise error; MD, mean diffusivity; MRI, magnetic resonance imaging; RD, radial diffusivity; ROI, region of interest; RT, repetition time; Brain areas: ACC, anterior cingulate cortex; CG, cingulate gyrus; DLPFC, dorsolateral prefrontal cortex; FC, frontopolar cortex; FG, frontal gyrus; FOC, frontal operculum cortex; IC, insular cortices; IFG, inferior frontal gyrus; IPC, inferior parietal cortex; LG, lingual gyrus; M1, primary motor cortex; MC, motor cortex; MFC, middle frontal cortex; MFG, middle frontal gyrus; MOC, medial orbitofrontal cortex; MTG, middle temporal gyrus; OFC, orbito frontal cortex; PC, premotor cortex; PCG, posterior cingulate gyrus; PG, parahippocampal gyrus; POC, parietal operculum cortex; PoG, postcentral gyrus; PrG, precentral gyrus; SC, sensorimotor cortex SFG; superior frontal gyrus; SG, supramarginal gyrus; SLF, superior longitudinal fasciculus; SMA, supplementary motor area; STG, superior temporal gyri; TPC, temporoparietal cortex; TPJ, temporoparietal junction.
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A fast computational framework for genome-wide association studies with neuroimaging data

A fast computational framework for genome-wide association studies with neuroimaging data

Speedup: 227 Figure 3. Setting and execution of the Map-Reduce algorithm on the cluster. Results on simulated data We simulate functional Magnetic Resonance Images (fMRI) from real genetic data obtained from the Imagen database [8]. We use the number of minor alleles for each SNP and we assume an additive genetic model. Ten random SNPs produce an effect in a spherical brain region, centered at random positions in the standard space, then intersected with the support of grey matter using a mask computed for the Imagen dataset (see below). We add i.i.d. Gaussian noise, smoothed spatially with a Gaussian kernel (σ = 3mm), to model other variability sources. The effect size and the Signal-to-Noise Ratio (SNR) can vary across simulations.
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Neural signature of DCD: a critical review of MRI neuroimaging studies

Neural signature of DCD: a critical review of MRI neuroimaging studies

Using structural MRI, this study aimed to address the question of whether abnormal connectivity in DCD overlaps with that seen in ASD or comorbid DCD + ASD. The authors investigated differences in the global and regional topological properties of structural brain networks (small-world networks between 68 brain regions, based on cortical thickness) in 53 children divided in four groups. Between-group differences between ASD (with or without DCD) and other groups were large, but are not pro- vided here (see Caeyenberghs et al. ( 80 ) for details). The DCD group exhibited only one difference from the TD group, in the right lateral orbitofrontal cortex (higher clustering coefficient). Regarding comorbidity, compared with the DCD group, the chil- dren with DCD + ASD had higher nodal clustering coefficients in the left lingual gyrus, pars opercularis of the left IFG, left tem- poral pole, right entorhinal cortex, and right MOC. By contrast, the DCD + ASD group had lower clustering coefficients in the right posterior cingulate gyrus, right postcentral gyrus, and left transverse temporal gyrus. The authors concluded that the DCD, ASD, and TD groups had prominent small-world properties in their cortical thickness networks, even if the overall organization of networks in the children with DCD was relatively intact, as shown by the absence of group effects on overall network param- eters (global network values close to those of TD).
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Challenges and Perspectives of the Hybridization of PET with Functional MRI or Ultrasound for Neuroimaging

Challenges and Perspectives of the Hybridization of PET with Functional MRI or Ultrasound for Neuroimaging

is also highly relevant, at least in a preclinical setting, since molecular targets expressed by the brain vasculature facing the blood are easily accessible by large contrast-carrying particles (Gauberti et al., 2018). The increasing diversity of MR- dedicated molecular imaging probes can be combined with MRS and PET to provide a larger and multiparametric landscape of the molecular environment in certain brain regions, which may be useful to untangle complex signaling processes, in a longitudinal way. In this framework, magnetic particle imaging (MPI) is increasingly regarded as an alternative imaging technique to selectively track and quantify magnetic nanoparticles in the brain (Wu et al., 2019). Human-sized MPI has been recently proposed as mean to explore the neurovascular system, with great potential for hybridization with complimentary neuroimaging techniques (Graeser et al., 2019) . The clinical pharmacodynamics of many CNS-acting drugs is difficult to predict (Srinivas et al., 2018). PET and phMRI have played an unevaluable role to elucidate the mechanism of action of drugs at the CNS level, with rapid clinical perspectives (Suridjan et al., 2019). In neuropharmacology, a large variability in response exist, which mechanisms remains misunderstood. This variability may involve the downstream processes of pharmacokinetics (i.e ability of the drug to reach the target tissue), interaction with the CNS target, or transduction of the neuroreceptor signaling (Srinivas et al., 2018). Conventional PET studies using target-specific radioligands are useful to estimate baseline target availability and drug induced target engagement. This strategy offers an indirect but target-restricted insight into the effects of drugs to the brain. Isotopic radiolabeling of drugs is sometime possible, which enables direct consideration of the brain kinetics, including transport across the BBB, as factor of variability for pharmacodynamics (Tournier et al., 2018; Bauer et al., 2019). In this framework, hybridization of PET imaging with other functional neuroimaging techniques has a great potential to unveil neurovascular coupling in complex situations such the desensitization of neuroreceptor (Sander et al., 2016) or the action of biased agonists (Vidal et al., 2018), during which a disconnection between regional target engagement and signal transduction may exist.
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Revolution of Resting-State Functional Neuroimaging Genetics in Alzheimer’s Disease

Revolution of Resting-State Functional Neuroimaging Genetics in Alzheimer’s Disease

Concluding Remarks and Future Perspectives Overall, evidence is building that several genes associated with AD risk are able to differentially disrupt brain functional connectivity at rest in CN, presymptomatic, and symptomatic AD individuals [72]. Such neural differences are detectable in CN mutation carriers of APOE, PICALM, CLU, and BIN1 genes across the lifespan. Relatively consistent at-rest functional neuroimaging data showed decreased connectivity in the middle and posterior DMN regions, including PCC and Pcu, and increased DMN connectivity in the frontal and lateral structures, such as the middle temporal and the prefrontal cortices. Additional functional connectivity alterations associated with the APOE polymorphism were identified in the salience [49] and auditory systems [43]. Accordingly, presymptomatic AD individuals exhibited abnormalities in the DMN [61,62], even at a very young age [59,60]. In contrast, significant results were not consistently reported in symptomatic AD dementia patients [61,62], despite two studies reported a selective alteration of the DMN [22,23] and the executive control network [23].
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Functional studies of Frataxin : relations with metal homeostasis and oxidatives stress

Functional studies of Frataxin : relations with metal homeostasis and oxidatives stress

his-tag, which can avoid the unexpected binding phenomenon. However, the purification demands several steps and can lead to some secondary effects. Preliminary attempts to produce recombinant Yfh1 using a classical pET21b expression vector failed, mostly because the IPTG mediated induction of the protein production was poorly efficient, and the protein was purified together with major chaperones from E. coli (DnaJ, IbpA) identified by peptide mass fingerprints from SDS-PAGE separated proteins (works of others colleagues in group). We therefore cloned the open reading frame of the mature Yfh1 into the pSBET-b expression vector that carries the ArgU gene, allowing efficient production of eukaryote proteins in E. coli. The auto-induction medium together with the two-steps purification strategy taking advantage of the low pI (4.13) of the protein, allowed us to produce up to 10 mg purified protein / g cell paste. After the purification, Yfh1 has been found in dimer form as being confirmed by MS and size-exclusion chromatography. Even if frataxin from a psychrophilic bacterium has shown to be mainly in monomeric form, a fraction has been found to be dimeric at high concentration (Noguera et al. 2015). In addition, in vitro, Yfh1 was susceptible to homo-oligomerization without iron 2 weeks after its isolation (Cook et al. 2006). To our knowledge, in the absence of metal this oligomerization has not been observed in vivo.
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Pattern Recognition in NeuroImaging: What can machine learning classifiers bring to the analysis of functional brain imaging?

Pattern Recognition in NeuroImaging: What can machine learning classifiers bring to the analysis of functional brain imaging?

However, distinguishing between the three tasks using both groups led to signifi- cant results when considering the whole brain and motor masks, suggesting that the between-subject variability within one group is large compared to the between- groups variability for those features. This result highlights the importance of fea- ture selection in the present case and favours the use of wrapper or embedded feature selection techniques to increase the performance of the machine learning based models. Regarding the Parkinsonian group, the between-subjects variability might further be explained by the heterogeneity of the gait disorders in patients. Distinguishing between patients with light or severe gait disorders, for example by considering the Freezing of Gait (FoG, Karachi et al., 2010), might increase the ratio of between versus within group variability and thereby improve the clas- sification. Another issue to consider for diagnostic purposes is disease duration; there was a large inter-individual variability in disease duration (and severity, see Cremers et al., 2012b for a table presenting different disease parameters for each patient). A possible improvement would hence be the inclusion of de novo patients (i.e. early stage patients). Finally, medication was another confounding factor since all patients were scanned on medication, with a variability in the equivalent doses of medicine across subjects. In conclusion to this comment, the inclusion of early stage de novo patients who are not yet treated should decrease the within- group variability and thereby might improve the performance of the classification.
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IMPACT OF THE JOINT DETECTION-ESTIMATION APPROACH ON RANDOM EFFECTS GROUP STUDIES IN FMRI

IMPACT OF THE JOINT DETECTION-ESTIMATION APPROACH ON RANDOM EFFECTS GROUP STUDIES IN FMRI

This paper is structured as follows. For the sake of self- consistency, the classical fMRI analysis framework is sum- marized in Section 2. The JDE approach is presented in Sec- tion 3. It relies on a prior parcellation of fMRI data, which derives from a clustering procedure that preserves connectiv- ity and functional homogeneity. Then, at the parcel level the JDE framework allows us to specify and estimate a specific BOLD model. Section 4 is devoted to group studies in fMRI where the principles of random effect analysis are reminded. In Section 5, results obtained at the group level using different subject-level inferences are compared on two salient contrasts of interest of a quick fMRI mapping experiment. A special at- tention is paid to the HRF variability in the motor and parietal regions. Conclusions are drawn in Section 6.
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Impact of the joint detection-estimation approach on random effects group studies in fMRI

Impact of the joint detection-estimation approach on random effects group studies in fMRI

This paper is structured as follows. For the sake of self- consistency, the classical fMRI analysis framework is sum- marized in Section 2. The JDE approach is presented in Sec- tion ??. It relies on a prior parcellation of fMRI data, which derives from a clustering procedure that preserves connectiv- ity and functional homogeneity. Then, at the parcel level the JDE framework allows us to specify and estimate a specific BOLD model. Section ?? is devoted to group studies in fMRI where the principles of random effect analysis are reminded. In Section ??, results obtained at the group level using dif- ferent subject-level inferences are compared on two salient contrasts of interest of a quick fMRI mapping experiment. A special attention is paid to the HRF variability in the motor and parietal regions. Conclusions are drawn in Section ??.
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Modification of both functional neurological symptoms and neuroimaging patterns with a good anatomoclinical concordance: a case report

Modification of both functional neurological symptoms and neuroimaging patterns with a good anatomoclinical concordance: a case report

A functional neuroimaging study compares feigners and FND in sensorimotor deficit. A Go/no Go paradigm elicited an activation of the right inferior frontal gyrus during simulated weakness in feigners compared with motor FND (42). Other studies found an implication of the supplementary motor area, premotor cortex, cingulate cortex and right and medial prefrontal cortex during preparation and execution of the movement (43–46). Neural underpinning of feigners is clearly different from FND one, but it is not well described, possibly due to various paradigms and a weak number of subjects. No functional imaging defect was reported during malingering or feigners, supporting the diagnosis of FND and, in our case, presenting with hypoperfusions. These results suggest that functional imaging (especially hypoperfusions) may be an interesting biomarker to support diagnosis.
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The specific role of serial order short-term memory in calculation abilities: a developmental functional neuroimaging study

The specific role of serial order short-term memory in calculation abilities: a developmental functional neuroimaging study

Behavioral studies have highlighted the importance of distinguishing item and serial order short-term memory (STM) components for studying the role of verbal STM in numerical development (Attout et al., 2014ab; 2015). These studies demonstrated that the serial order STM predicted calculation abilities one and two years later in kindergarten children but also that a deficit to process serial order information in STM was observed in children and adults with dyscalculia and this, in contrast to item STM. These data are in favor of a specific implication of serial order STM in calculation abilities.
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Bayesian Estimation of Probabilistic Atlas for Anatomically-Informed Functional MRI Group Analyses

Bayesian Estimation of Probabilistic Atlas for Anatomically-Informed Functional MRI Group Analyses

2 H. Xu et al. Functional Magnetic Resonance Imaging of the brain is used to localize func- tional areas in the cortex and deep nuclei by measuring MRI signal changes associated with neural activity. It is a tool of choice for cognitive studies that aim at identifying specific regions of the brain that are activated in percep- tual, cognitive or motor tasks. The most popular type of analysis is Statistical Parametric Mapping (SPM) [5], an approach that estimates the probability that some activation can be due to chance alone and provides p-value maps. Group analysis is then used to detect regions that show a positive mean activation across subjects [4, 12]. Accurate realignment of individual scans is most often obtained by normalizing individual anatomical images to a T1 MRI template. These processing steps are done without considering the complementarity of the anatomical and functional information available in each subject. Therefore, detected activations are not confined to gray matter. Few fMRI segmentation methods have been proposed to take into account multi-modal data, such as T1 and functional MRI. An implementation of cortical-based analysis of fMRI data was proposed in [2]. The fMRI data is mapped to the cortical surface, then ac- tivations are detected on the surface. It has been shown to achieve anatomically accurate activation detection. In [8], Markov Random Fields (MRF) were used as a spatial regularization in fMRI detection and anatomical information was incorporated into the MRF-based detection framework. In [11], both anatomical and functional data are used to improve the group-wise registrations. Anatom- ical information appears helpful in fMRI detection; however, the approaches so far do not incorporate a group model into the analysis. In this paper, we process multi-modal data jointly to ensure that the detected active areas are conditioned to gray matter while registration is informed by functional information. More specifically, group analysis first performs the realignment of individual images to a T1 MRI template and then segments active regions by thresholding. However, performing registration and segmentation jointly is generally more effective than performing them sequentially [13, 14]. In this paper, we take advantage of such coupling.
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Functional – structural plant models: a growing paradigm for plant studies

Functional – structural plant models: a growing paradigm for plant studies

The contributions within this issue in the area of morphologic- al modelling belong to the latter category. Abera et al. (2014) for- mulate a generic model that can account for both symmetrically and asymmetrically dividing cells with isotropic and anisotropic growth. The cells are modelled as closed, thin-walled structures that maintain tension by turgor pressure. The model can produce tissues that have different topological and geometrical proper- ties, and it will be useful for in silico investigations of plant cell division. Cartenı` et al. (2014) study formation of vascular tissues in growing plant stems. They present a spatially explicit reaction – diffusion model defining a set of logical and functional rules to simulate the differentiation of procambium, phloem and xylem and their spatial and temporal patterns from a group of un- differentiated cells. This shows that common genetic – molecular machinery can create different spatial patterns of plant vascular development. The model can be used to test different hypotheses of genetic and molecular interactions involved in the development of vascular tissues. Dale et al. (2014) focus on the surface of plant stems: they present a dynamic model of grasstree development that captures both phyllotactic patterns of leaf bases during primary growth and the changes in the trunk’s width during secondary growth. A biomechanical model component simulates emergence of fractures during expansion of stem girth. The model produces similar fracture patterns as those seen in real trees, supporting the hypothesis that bark pattern formation is primarily a biomech- anical phenomenon.
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