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Kwok S.C.,East China Normal University | Kwok S.C.,Shanghai University | Kwok S.C.,Neuroimaging Laboratory | Macaluso E.,East China Normal University | Macaluso E.,Shanghai University
Human Brain Mapping | Year: 2015

We investigated the neural correlates supporting three kinds of memory judgments after very short delays using naturalistic material. In two functional magnetic resonance imaging (fMRI) experiments, subjects watched short movie clips, and after a short retention (1.5-2.5 s), made mnemonic judgments about specific aspects of the clips. In Experiment 1, subjects were presented with two scenes and required to either choose the scene that happened earlier in the clip ("scene-chronology"), or with a correct spatial arrangement ("scene-layout"), or that had been shown ("scene-recognition"). To segregate activity specific to seen versus unseen stimuli, in Experiment 2 only one probe image was presented (either target or foil). Across the two experiments, we replicated three patterns underlying the three specific forms of memory judgment. The precuneus was activated during temporal-order retrieval, the superior parietal cortex was activated bilaterally for spatial-related configuration judgments, whereas the medial frontal cortex during scene recognition. Conjunction analyses with a previous study that used analogous retrieval tasks, but a much longer delay (>1 day), demonstrated that this dissociation pattern is independent of retention delay. We conclude that analogous brain regions mediate task-specific retrieval across vastly different delays, consistent with the proposal of scale-invariance in episodic memory retrieval. © 2015 Wiley Periodicals, Inc. Source

Jones D.K.,University of Cardiff | Cercignani M.,Neuroimaging Laboratory
NMR in Biomedicine | Year: 2010

Obtaining reliable data and drawing meaningful and robust inferences from diffusion MRI can be challenging and is subject to many pitfalls. The process of quantifying diffusion indices and eventually comparing them between groups of subjects and/or correlating them with other parameters starts at the acquisition of the raw data, followed by a long pipeline of image processing steps. Each one of these steps is susceptible to sources of bias, which may not only limit the accuracy and precision, but can lead to substantial errors. This article provides a detailed review of the steps along the analysis pipeline and their associated pitfalls. These are grouped into 1 pre-processing of data; 2 estimation of the tensor; 3 derivation of voxelwise quantitative parameters; 4 strategies for extracting quantitative parameters; and finally 5 intra-subject and inter-subject comparison, including region of interest, histogram, tract-specific and voxel-based analyses. The article covers important aspects of diffusion MRI analysis, such as motion correction, susceptibility and eddy current distortion correction, model fitting, region of interest placement, histogram and voxel-based analysis. We have assembled 25 pitfalls (several previously unreported) into a single article, which should serve as a useful reference for those embarking on new diffusion MRI-based studies, and as a check for those who may already be running studies but may have overlooked some important confounds. While some of these problems are well known to diffusion experts, they might not be to other researchers wishing to undertake a clinical study based on diffusion MRI. © 2010 John Wiley & Sons, Ltd. Source

Knyazeva M.G.,University of Lausanne | Jalili M.,Sharif University of Technology | Frackowiak R.S.,University of Lausanne | Frackowiak R.S.,Neuroimaging Laboratory | Rossetti A.O.,University of Lausanne
Journal of Neurology, Neurosurgery and Psychiatry | Year: 2011

Objective Psychogenic non-epileptic seizures (PNES) are paroxysmal events that, in contrast to epileptic seizures, are related to psychological causes without the presence of epileptiform EEG changes. Recent models suggest a multifactorial basis for PNES. A potentially paramount, but currently poorly understood factor is the interplay between psychiatric features and a specific vulnerability of the brain leading to a clinical picture that resembles epilepsy. Hypothesising that functional cerebral network abnormalities may predispose to the clinical phenotype, the authors undertook a characterisation of the functional connectivity in PNES patients. Methods The authors analysed the whole-head surface topography of multivariate phase synchronisation (MPS) in interictal high-density EEG of 13 PNES patients as compared with 13 age- and sex-matched controls. MPS mapping reduces the wealth of dynamic data obtained from high-density EEG to easily readable synchronisation maps, which provide an unbiased overview of any changes in functional connectivity associated with distributed cortical abnormalities. The authors computed MPS maps for both Laplacian and common-averagereference EEGs. Results In a between-group comparison, only patchy, non-uniform changes in MPS survived conservative statistical testing. However, against the background of these unimpressive group results, the authors found widespread inverse correlations between individual PNES frequency and MPS within the prefrontal and parietal cortices. Interpretation PNES appears to be associated with decreased prefrontal and parietal synchronisation, possibly reflecting dysfunction of networks within these regions. Source

Lenzi D.,University of Rome La Sapienza | Trentini C.,University of Rome La Sapienza | Pantano P.,University of Rome La Sapienza | Macaluso E.,Neuroimaging Laboratory | And 2 more authors.
Human Brain Mapping | Year: 2013

Background: The attachment model, as assessed by means of the Adult Attachment Interview (AAI), is crucial for understanding emotion regulation and feelings of security in human interactions as well as for the construction of the caregiving system. The caregiving system is a set of representations about affiliative behaviors, guided by sensitivity and empathy, and is fully mature in young-adulthood. Here, we examine how different attachment models influence brain responses in areas related to empathy and emotions in young-adult subjects with secure and dismissing attachment models. Methods: By means of AAI, we selected 11 nulliparous young-adult females with a secure model and 12 with a dismissing model. Subjects underwent functional magnetic resonance, whereas imitating or observing and empathizing with infant facial expressions. Subjects were tested for alexithymia and reflective functioning. Results: Dismissing subjects activated motor, mirror, and limbic brain areas to a significantly greater extent, but deactivated the medial orbitofrontal cortex (mOFC) and the perigenual anterior cingulated cortex (pACC). During emotional faces, increased activity in dismissing women was seen in the right temporal pole. Furthermore, greater alexithymia was correlated with greater activity in the entorhinal cortex and greater deactivation in the pACC/mOFC. Conclusions: These findings provide evidence of how the attachment model influences brain responses during a task eliciting attachment. In particular, hyperactivation of limbic and mirror areas may reflect emotional dysregulation of infantile experiences of rejection and lack of protection, whereas increased deactivation of fronto-medial areas may be the expression of the inhibition of attachment behaviors, which is a typical aspect of dismissing attachment. © 2012 Wiley Periodicals, Inc. Source

Bordier C.,Neuroimaging Laboratory | Macaluso E.,Neuroimaging Laboratory
Human Brain Mapping | Year: 2015

Several methods are available for the identification of functional networks of brain areas using functional magnetic resonance imaging (fMRI) time-series. These typically assume a fixed relationship between the signal of the areas belonging to the same network during the entire time-series (e.g., positive correlation between the areas belonging to the same network), or require a priori information about when this relationship may change (task-dependent changes of connectivity). We present a fully data-driven method that identifies transient network configurations that are triggered by the external input and that, therefore, include only regions involved in stimulus/task processing. Intersubject synchronization with short sliding time-windows was used to identify if/when any area showed stimulus/task-related responses. Next, a first clustering step grouped together areas that became engaged concurrently and repetitively during the time-series (stimulus/task-related networks). Finally, for each network, a second clustering step grouped together all the time-windows with the same BOLD signal. The final output consists of a set of network configurations that show stimulus/task-related activity at specific time-points during the fMRI time-series. We label these configurations: "brain modes" (bModes). The method was validated using simulated datasets and a real fMRI experiment with multiple tasks and conditions. Future applications include the investigation of brain functions using complex and naturalistic stimuli. © 2015 Wiley Periodicals, Inc. Source

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