13 research outputs found

    García, Xavier (ed.) (2015). Joan Oliver-Joaquim Molas: Diàleg epistolar il·lustrat (1959-1982). Lleida: Pagès Editors, pp. 186

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    <p><i>Objectives</i>: Attention-deficit/hyperactivity disorder (ADHD) has been associated with spatial working memory as well as frontostriatal core deficits. However, it is still unclear how the link between these frontostriatal deficits and working memory function in ADHD differs in children and adults. This study examined spatial working memory in adults and children with ADHD, focussing on identifying regions demonstrating age-invariant or age-dependent abnormalities. <i>Methods</i>: We used functional magnetic resonance imaging to examine a group of 26 children and 35 adults to study load manipulated spatial working memory in patients and controls. <i>Results</i>: In comparison to healthy controls, patients demonstrated reduced positive parietal and frontostriatal load effects, i.e., less increase in brain activity from low to high load, despite similar task performance. In addition, younger patients showed negative load effects, i.e., a decrease in brain activity from low to high load, in medial prefrontal regions. Load effect differences between ADHD and controls that differed between age groups were found predominantly in prefrontal regions. Age-invariant load effect differences occurred predominantly in frontostriatal regions. <i>Conclusions</i>: The age-dependent deviations support the role of prefrontal maturation and compensation in ADHD, while the age-invariant alterations observed in frontostriatal regions provide further evidence that these regions reflect a core pathophysiology in ADHD.</p

    NBT: NBT v0.5.0-alpha Integrating Biomarkers.

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    <p>Changelog (In total 45 commits and changes to 368 files since NBT v0.4.2-alpha):</p> <ul> <li>New tools for integrating biomarker.</li> <li>Correcting critical bug in eeg_eegrej (eeg_eegrej.m did not combine regions correctly - see commit log)</li> <li>New biomarker: Phase Lag Index</li> <li>New biomarker: Hjorth's parameters.</li> <li>New option to plot a grand average power spectrum</li> <li>upgraded EEGLAB to 13.3.2</li> <li>Multiple minor bug fixes.</li> </ul

    Women show higher perfusion than men and DHEAS correlates negatively with perfusion.

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    <p>a) Sex difference in whole brain grey matter perfusion: perfusion is higher in women (<i>M</i> = 35.97 ml/min/100 ml, <i>SD</i> = 5.37) than in men (<i>M</i> = 30.47 ml/min/100 ml, <i>SD</i> = 5.91, <i>p</i> = .006). Single dots represent the subjects' individual values. The horizontal line within the boxes indicate medians, the edges of the boxes are the 25<sup>th</sup> and 75<sup>th</sup> percentiles, and the whiskers represent 1.5 times the interquartile range. b) Sex difference (women > men) in regional perfusion: women show higher regional perfusion than men (<i>p</i> = .004, FWE-corrected). c) Simple regression analysis with whole brain perfusion values as the dependent variable and DHEAS as the only predictor: a significant model was found (<i>p</i> = .007, adjusted <i>R</i><sup><i>2</i></sup> = .180) with a standardised β = -.452 for DHEAS. d) DHEAS effects in men and women: DHEAS correlates negatively with regional perfusion in both sexes (<i>p</i> = .004, FWE-corrected). Colour bar in a) and c) denotes a non-parametric <i>t</i> score, given by <i>a1</i>/[standard error(<i>a1</i>)], see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135827#sec002" target="_blank">methods</a>. Images are shown in neurological orientation. Slices are at MNI z-coordinates -45, -30, -15, 0, 15, 30, 45, 60, 75 (from top left to bottom right).</p

    The more random the AP train, the shorter the mean HAE duration and the longer the mean LAE duration.

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    <p>Mean HAE and LAE durations (SEM) in the excitatory population (a, b) and the inhibitory population (c, d) for different values of AP randomness. Red lines, exponential fits.</p

    Quantification of high-amplitude episodes (HAEs) and low-amplitude episodes (LAEs) in network oscillations.

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    <p>(a) Raster diagram showing the firing times (indicated by dots) of the excitatory cells. (b) Corresponding firing-rate histogram. The maximal firing rate (red bar) per oscillation period <i>T</i> is successively determined by using a sliding time window of length <i>T</i>. The time axis is discretized into bins of 6 ms. (c) A spline polynomial is interpolated through the maximal firing rates (red bars) per oscillation period. Time intervals during which the curve exceeds the HAE threshold (dashed line) are considered HAEs, otherwise LAEs. (See further <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002666#s2" target="_blank">Methods</a>.)</p

    The higher the AP frequency, the shorter the mean HAE duration and the longer the mean LAE duration.

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    <p>Mean HAE and LAE durations (SEM) in the excitatory population (a, b) and the inhibitory population (c, d) for different values of AP frequency. Red lines, exponential fits.</p

    During a LAE, for both the excitatory and the inhibitory population, cell firing is less synchronous.

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    <p>This is revealed by the spread of activity over more time bins and the diminished overlap in membrane potential traces. In addition, fewer cells are firing during a LAE. Shown are the raster diagram of cell firing (a, d), the firing rate histogram with the spline polynomial (b, e), and the cell membrane potentials (c, f) of and interval of activity from <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002666#pcbi-1002666-g003" target="_blank">Fig. 3</a>. Horizontal dashed line, HAE threshold.</p

    Alternating episodes of high- and low-amplitude oscillations for two different values of AP randomness.

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    <p>Raster diagrams of cell firing (a, d), firing-rate histograms with interpolated spline polynomials (b, e) and wavelet transform of the firing-rate histograms (c, f) for the excitatory population for AP randomness 0.7 (a–c) and 0 (d–f) in the minimal stimulation protocol. For rand = 0, APs were simultaneously delivered to all I cells at regular intervals of 90 ms.</p

    The distributions of HAE duration in the model match those observed for carbachol-induced oscillations in rat prefrontal cortex.

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    <p>(a) The model distributions (red lines) in the excitatory population and the empirical distributions (histograms) observed in the prelimbic (PrL) and infralimbic (IL) regions of the prefrontal cortex <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002666#pcbi.1002666-vanAerde3" target="_blank">[52]</a>. In each region of the PFC, both fast and slow oscillations occurred, which both exhibited HAE-LAE alternations. The oscillation frequency in the model was adjusted by changing the IPSC decay time Ď„. The distributions were normalized by dividing the number of HAEs within a given bin by the total number of HAEs in the distribution. (b) The cumulative distributions of the model data (red lines) and the empirical data (black lines). The model distributions are not significantly different (Kolmogorov-Smirnov test) from the empirical distributions. (c) The distributions generated by a Markov process (green line) accurately describe the empirical distributions (histograms). Parameter is the probability that the first oscillation cycle with high amplitude (upstate) in a HAE is followed by an upstate; is the probability that an upstate in the rest of the HAE is followed by an upstate. See further main text.</p

    APs can disrupt synchrony among I cells, causing a LAE.

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    <p>(a) Red, external spikes (AP). Blue, inhibitory spikes. (top) AP frequency was the same as the frequency of the ongoing oscillation (17.63 Hz). If the first AP was delivered (at t<sub>onset</sub> = 330 ms) when the membrane potential of the I cells was close to the firing threshold (between 0 and 0.7 mV), I cell firing was slightly advanced, but cells kept firing in synchrony. (middle panel) If the first APs was delivered when the membrane potential of the I cells was further below firing threshold (between 1 and 1.5 mV), I cell firing was reset and temporarily lost synchrony. (bottom) If AP frequency was lower than the frequency of the ongoing oscillation, the likelihood of APs resetting I cell firing increased, generating HAE-to-LAE transitions. (b) The firing pattern of a representative I cell for the different cases in (a). (c) The APs advanced the firing of the I cells compared with the expected firing dictated by the ongoing oscillation. The advancement depended on the cell's membrane potential at the time of AP arrival. The vertical line indicates the firing threshold.</p
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