20 resultados para Sulcus vocalis


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Purpose: Aqueous shunt implantation into the anterior chamber is associated with corneal decompensation in up to a third of eyes. Intracameral tube position may affect corneal endothelial cell loss. The authors set out to examine the efficacy and safety of Baerveldt shunt implantation into the ciliary sulcus combined with surgical peripheral iridectomy (SPI). Methods: One hundred eyes prospectively underwent Baerveldt shunt implantation into the cilliary sulcus combined with SPI, leaving a short intracameral tube length (1-2mm). Pre and post operative measures recorded included patient demographics, visual acuity, IOP, number of glaucoma medications (GMs) and all complications. Pre-existing corneal decompensation was recorded. Success was defined as IOP≤21mmHg and 20% reduction in IOP from baseline with or without GMs. Results: Mean age was 65.4 years (±20.4years). Mean follow-up was 10.8 months. Preoperatively IOP was 25.7mmHg (± 9.9mmHg), GMs were 2.9 (±1.2) and VA was 0.4 (±0.3). At one year postoperatively there was a significant drop in IOP (mean= 13.3mmHg (± 5.0mmHg); p<0.001) and number of GMs (mean= 1.3 (±1.4); p<0.001); and no significant change in VA (mean= 0.4 (±0.3); p=0.93). The success rate at one year was 83%. Complications were minor and non sight threatening (10%), there were no cases of postoperative corneal decompensation, tube blockage or iris/corneal-tube contact. Conclusions: The results demonstrate that placement of Baerveldt shunts into the ciliary sulcus with SPI is a safe and efficacious method of IOP reduction in comparison with standard shunt positioning in the anterior chamber. The intracameral tube position combined with SPI avoided tube-iris contact and corneal decompensation. Sulcus placement of aqueous shunts should be considered in pseudophakic eyes.

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PURPOSE: To assess the agreement and repeatability of horizontal white-to-white (WTW) and horizontal sulcus-to-sulcus (STS) diameter measurements and use these data in combination with available literature to correct for interdevice bias in preoperative implantable collamer lens (ICL) size selection. DESIGN: Interinstrument reliability and bias assessment study. METHODS: A total of 107 eyes from 56 patients assessed for ICL implantation at our institution were included in the study. This was a consecutive series of all patients with suitable available data. The agreement and bias between WTW (measured with the Pentacam and BioGraph devices) and STS (measured with the HiScan device) were estimated. RESULTS: The mean spherical equivalent was -8.93 ± 5.69 diopters. The BioGraph measures of WTW were wider than those taken with the Pentacam (bias = 0.26 mm, P < .01), and both horizontal WTW measures were wider than the horizontal STS measures (bias >0.91 mm, P < .01). The repeatability (Sr) of STS measured with the HiScan was 0.39 mm, which was significantly reduced (Sr = 0.15 mm) when the average of 2 measures was used. Agreement between the horizontal WTW measures and horizontal STS estimates when bias was accounted for was г = 0.54 with the Pentacam and г = 0.64 with the BioGraph. CONCLUSIONS: Large interdevice bias was observed for WTW and STS measures. STS measures demonstrated poor repeatability, but the average of repeated measures significantly improved repeatability. In order to conform to the US Food and Drug Administration's accepted guidelines for ICL sizing, clinicians should be aware of and account for the inconsistencies between devices.

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Objectives: Our aim was to study the brain regions involved in a divided attention tracking task related to driving in occasional cannabis smokers. In addition we assessed the relationship between THC levels in whole blood and changes in brain activity, behavioural and psychomotor performances. Methods: Twenty-one smokers participated to two independent cross-over fMRI experiments before and after smoking cannabis and a placebo. The paradigm was based on a visuo-motor tracking task, alternating active tracking blocks with passive tracking viewing and rest condition. Half of the active tracking conditions included randomly presented traffic lights as distractors. Blood samples were taken at regular intervals to determine the time-profiles of the major cannabinoids. Their levels during the fMRI experiments were interpolated from concentrations measured by GCMS/ MS just before and after brain imaging. Results: Behavioural data, such as the discard between target and cursor, the time of correct tracking and the reaction time during traffic lights appearance showed a statistical significant impairment of subject s skills due to THC intoxication. Highest THC blood concentrations were measured soon after smoking and ranged between 28.8 and 167.9 ng/ml. These concentrations reached values of a few ng/ml during the fMRI. fMRI results pointed out that under the effect of THC, high order visual areas (V3d) and Intraparietal sulcus (IPS) showed an higher activation compared to the control condition. The opposite comparison showed a decrease of activation during the THC condition in the anterior cingulate gyrus and orbitofrontal areas. In these locations, the BOLD showed a negative correlation with the THC level. Conclusion: Acute cannabis smoking significantly impairs performances and brain activity during active tracking tasks, partly reorganizing the recruitment of brain areas of the attention network. Neural activity in the anterior cingulate might be responsible of the changes in the cognitive controls required in our divided attention task.

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Introduction: Discrimination of species-specific vocalizations is fundamental for survival and social interactions. Its unique behavioral relevance has encouraged the identification of circumscribed brain regions exhibiting selective responses (Belin et al., 2004), while the role of network dynamics has received less attention. Those studies that have examined the brain dynamics of vocalization discrimination leave unresolved the timing and the inter-relationship between general categorization, attention, and speech-related processes (Levy et al., 2001, 2003; Charest et al., 2009). Given these discrepancies and the presence of several confounding factors, electrical neuroimaging analyses were applied to auditory evoked-potential (AEPs) to acoustically and psychophysically controlled non-verbal human and animal vocalizations. This revealed which region(s) exhibit voice-sensitive responses and in which sequence. Methods: Subjects (N=10) performed a living vs. man-made 'oddball' auditory discrimination task, such that on a given block of trials 'target' stimuli occurred 10% of the time. Stimuli were complex, meaningful sounds of 500ms duration. There were 120 different sound files in total, 60 of which represented sounds of living objects and 60 man-made objects. The stimuli that were the focus of the present investigation were restricted to those of living objects within blocks where no response was required. These stimuli were further sorted between human non-verbal vocalizations and animal vocalizations. They were also controlled in terms of their spectrograms and formant distributions. Continuous 64-channel EEG was acquired through Neuroscan Synamps referenced to the nose, band-pass filtered 0.05-200Hz, and digitized at 1000Hz. Peri-stimulus epochs of continuous EEG (-100ms to 900ms) were visually inspected for artifacts, 40Hz low-passed filtered and baseline corrected using the pre-stimulus period . Averages were computed from each subject separately. AEPs in response to animal and human vocalizations were analyzed with respect to differences of Global Field Power (GFP) and with respect to changes of the voltage configurations at the scalp (reviewed in Murray et al., 2008). The former provides a measure of the strength of the electric field irrespective of topographic differences; the latter identifies changes in spatial configurations of the underlying sources independently of the response strength. In addition, we utilized the local auto-regressive average distributed linear inverse solution (LAURA; Grave de Peralta Menendez et al., 2001) to visualize and statistically contrast the likely underlying sources of effects identified in the preceding analysis steps. Results: We found differential activity in response to human vocalizations over three periods in the post-stimulus interval, and this response was always stronger than that to animal vocalizations. The first differential response (169-219ms) was a consequence of a modulation in strength of a common brain network localized into the right superior temporal sulcus (STS; Brodmann's Area (BA) 22) and extending into the superior temporal gyrus (STG; BA 41). A second difference (291-357ms) also followed from strength modulations of a common network with statistical differences localized to the left inferior precentral and prefrontal gyrus (BA 6/45). These two first strength modulations correlated (Spearman's rho(8)=0.770; p=0.009) indicative of functional coupling between temporally segregated stages of vocalization discrimination. A third difference (389-667ms) followed from strength and topographic modulations and was localized to the left superior frontal gyrus (BA10) although this third difference did not reach our spatial criterion of 12 continuous voxels. Conclusions: We show that voice discrimination unfolds over multiple temporal stages, involving a wide network of brain regions. The initial stages of vocalization discrimination are based on modulations in response strength within a common brain network with no evidence for a voice-selective module. The latency of this effect parallels that of face discrimination (Bentin et al., 2007), supporting the possibility that voice and face processes can mutually inform one another. Putative underlying sources (localized in the right STS; BA 22) are consistent with prior hemodynamic imaging evidence in humans (Belin et al., 2004). Our effect over the 291-357ms post-stimulus period overlaps the 'voice-specific-response' reported by Levy et al. (Levy et al., 2001) and the estimated underlying sources (left BA6/45) were in agreement with previous findings in humans (Fecteau et al., 2005). These results challenge the idea that circumscribed and selective areas subserve con-specific vocalization processing.

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Using optimized voxel-based morphometry, we performed grey matter density analyses on 59 age-, sex- and intelligence-matched young adults with three distinct, progressive levels of musical training intensity or expertise. Structural brain adaptations in musicians have been repeatedly demonstrated in areas involved in auditory perception and motor skills. However, musical activities are not confined to auditory perception and motor performance, but are entangled with higher-order cognitive processes. In consequence, neuronal systems involved in such higher-order processing may also be shaped by experience-driven plasticity. We modelled expertise as a three-level regressor to study possible linear relationships of expertise with grey matter density. The key finding of this study resides in a functional dissimilarity between areas exhibiting increase versus decrease of grey matter as a function of musical expertise. Grey matter density increased with expertise in areas known for their involvement in higher-order cognitive processing: right fusiform gyrus (visual pattern recognition), right mid orbital gyrus (tonal sensitivity), left inferior frontal gyrus (syntactic processing, executive function, working memory), left intraparietal sulcus (visuo-motor coordination) and bilateral posterior cerebellar Crus II (executive function, working memory) and in auditory processing: left Heschl's gyrus. Conversely, grey matter density decreased with expertise in bilateral perirolandic and striatal areas that are related to sensorimotor function, possibly reflecting high automation of motor skills. Moreover, a multiple regression analysis evidenced that grey matter density in the right mid orbital area and the inferior frontal gyrus predicted accuracy in detecting fine-grained incongruities in tonal music.

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Introduction : Driving is a complex everyday task requiring mechanisms of perception, attention, learning, memory, decision making and action control, thus indicating that involves numerous and varied brain networks. If many data have been accumulated over time about the effects of alcohol consumption on driving capability, much less is known about the role of other psychoactive substances, such as cannabis (Chang et al.2007, Ramaekers et al, 2006). Indeed, the solicited brain areas during safe driving which could be affected by cannabis exposure have not yet been clearly identified. Our aim is to study these brain regions during a tracking task related to driving skills and to evaluate the modulation due to the tolerance of cannabis effects. Methods : Eight non-smoker control subjects participated to an fMRI experiment based on a visuo-motor tracking task, alternating active tracking blocks with passive tracking viewing and rest condition. Half of the active tracking conditions included randomly presented traffic lights as distractors. Subjects were asked to track with a joystick with their right hand and to press a button with their left index at each appearance of a distractor. Four smoking subjects participated to the same fMRI sessions once before and once after smoking cannabis and a placebo in two independent cross-over experiments. We quantified the performance of the subjects by measuring the precision of the behavioural responses (i.e. percentage of time of correct tracking and reaction times to distractors). Functional MRI data were acquired using on a 3.0T Siemens Trio system equipped with a 32-channel head coil. BOLD signals will be obtained with a gradient-echo EPI sequence (TR=2s, TE=30ms, FoV=216mm, FA=90°, matrix size 72×72, 32 slices, thickness 3mm). Preprocessing, single subject analysis and group statistics were conducted on SPM8b. Results were thresholded at p<0.05 (FWE corrected) and at k>30 for spatial extent. Results : Behavioural results showed a significant impairment in task and cognitive test performance of the subjects after cannabis inhalation when comparing their tracking accuracy either to the controls subjects or to their performances before the inhalation or after the placebo inhalation (p<0.001 corrected). In controls, fMRI BOLD analysis of the active tracking condition compared to the passive one revealed networks of polymodal areas in superior frontal and parietal cortex dealing with attention and visuo-spatial coordination. In accordance to what is known of the visual and sensory motor networks we found activations in V4, frontal eye-field, right middle frontal gyrus, intra-parietal sulcus, temporo-parietal junction, premotor and sensory-motor cortex. The presence of distractors added a significant activation in the precuneus. Preliminary results on cannabis smokers in the acute phase, compared either to themselves before the cannabis inhalation or to control subjects, showed a decreased activation in large portions of the frontal and parietal attention network during the simple tracking task, but greater involvement of precuneus, of the superior part of intraparietal sulcus and middle frontal gyrus bilaterally when distractors were present in the task. Conclusions : Our preliminary results suggest that acute cannabis smoking alters performances and brain activity during active tracking tasks, partly reorganizing the recruitment of brain areas of the attention network.

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Visual perception of body motion is vital for everyday activities such as social interaction, motor learning or car driving. Tumors to the left lateral cerebellum impair visual perception of body motion. However, compensatory potential after cerebellar damage and underlying neural mechanisms remain unknown. In the present study, visual sensitivity to point-light body motion was psychophysically assessed in patient SL with dysplastic gangliocytoma (Lhermitte-Duclos disease) to the left cerebellum before and after neurosurgery, and in a group of healthy matched controls. Brain activity during processing of body motion was assessed by functional magnetic resonance imaging (MRI). Alterations in underlying cerebro-cerebellar circuitry were studied by psychophysiological interaction (PPI) analysis. Visual sensitivity to body motion in patient SL before neurosurgery was substantially lower than in controls, with significant improvement after neurosurgery. Functional MRI in patient SL revealed a similar pattern of cerebellar activation during biological motion processing as in healthy participants, but located more medially, in the left cerebellar lobules III and IX. As in normalcy, PPI analysis showed cerebellar communication with a region in the superior temporal sulcus, but located more anteriorly. The findings demonstrate a potential for recovery of visual body motion processing after cerebellar damage, likely mediated by topographic shifts within the corresponding cerebro-cerebellar circuitry induced by cerebellar reorganization. The outcome is of importance for further understanding of cerebellar plasticity and neural circuits underpinning visual social cognition.

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The kitten's auditory cortex (including the first and second auditory fields AI and AII) is known to send transient axons to either ipsi- or contralateral visual areas 17 and 18. By the end of the first postnatal month the transitory axons, but not their neurons of origin, are eliminated. Here we investigated where these neurons project after the elimination of the transitory axon. Eighteen kittens received early (postnatal day (pd) 2 - 5) injections of long lasting retrograde fluorescent traces in visual areas 17 and 18 and late (pd 35 - 64) injections of other retrograde fluorescent tracers in either hemisphere, mostly in areas known to receive projections from AI and AII in the adult cat. The middle ectosylvian gyrus was analysed for double-labelled neurons in the region corresponding approximately to AI and AII. Late injections in the contralateral (to the analysed AI, AII) hemisphere including all of the known auditory areas, as well as some visual and 'association' areas, did not relabel neurons which had had transient projections to either ipsi- or contralateral visual areas 17 - 18. Thus, AI and AII neurons after eliminating their transient juvenile projections to visual areas 17 and 18 do not project to the other hemisphere. In contrast, relabelling was obtained with late injections in several locations in the ipsilateral hemisphere; it was expressed as per cent of the population labelled by the early injections. Few neurons (0 - 2.5%) were relabelled by large injections in the caudal part of the posterior ectosylvian gyrus and the adjacent posterior suprasylvian sulcus (areas DP, P, VP). Multiple injections in the middle ectosylvian gyrus relabelled a considerably larger percentage of neurons (13%). Single small injections in the middle ectosylvian gyrus (areas AI, AII), the caudal part of the anterior ectosylvian gyrus and the rostral part of the posterior ectosylvian gyrus relabelled 3.1 - 7.0% of neurons. These neurons were generally near (&lt;2.0 mm) the outer border of the late injection sites. Neurons with transient projections to ipsi- or contralateral visual areas 17 and 18 were relabelled in similar proportions by late injections at any given location. Thus, AI or AII neurons which send a transitory axon to ipsi- or contralateral visual areas 17 and 18 are most likely to form short permanent cortical connections. In that respect, they are similar to medial area 17 neurons that form transitory callosal axons and short permanent axons to ipsilateral visual areas 17 and 18.

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The ability to discriminate conspecific vocalizations is observed across species and early during development. However, its neurophysiologic mechanism remains controversial, particularly regarding whether it involves specialized processes with dedicated neural machinery. We identified spatiotemporal brain mechanisms for conspecific vocalization discrimination in humans by applying electrical neuroimaging analyses to auditory evoked potentials (AEPs) in response to acoustically and psychophysically controlled nonverbal human and animal vocalizations as well as sounds of man-made objects. AEP strength modulations in the absence of topographic modulations are suggestive of statistically indistinguishable brain networks. First, responses were significantly stronger, but topographically indistinguishable to human versus animal vocalizations starting at 169-219 ms after stimulus onset and within regions of the right superior temporal sulcus and superior temporal gyrus. This effect correlated with another AEP strength modulation occurring at 291-357 ms that was localized within the left inferior prefrontal and precentral gyri. Temporally segregated and spatially distributed stages of vocalization discrimination are thus functionally coupled and demonstrate how conventional views of functional specialization must incorporate network dynamics. Second, vocalization discrimination is not subject to facilitated processing in time, but instead lags more general categorization by approximately 100 ms, indicative of hierarchical processing during object discrimination. Third, although differences between human and animal vocalizations persisted when analyses were performed at a single-object level or extended to include additional (man-made) sound categories, at no latency were responses to human vocalizations stronger than those to all other categories. Vocalization discrimination transpires at times synchronous with that of face discrimination but is not functionally specialized.

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Despite myriad studies, neurophysiologic mechanisms mediating illusory contour (IC) sensitivity remain controversial. Among the competing models one favors feed-forward effects within lower-tier cortices (V1/V2). Another situates IC sensitivity first within higher-tier cortices, principally lateral-occipital cortices (LOC), with later feedback effects in V1/V2. Still others postulate that LOC are sensitive to salient regions demarcated by the inducing stimuli, whereas V1/V2 effects specifically support IC sensitivity. We resolved these discordances by using misaligned line gratings, oriented either horizontally or vertically, to induce ICs. Line orientation provides an established assay of V1/V2 modulations independently of IC presence, and gratings lack salient regions. Electrical neuroimaging analyses of visual evoked potentials (VEPs) disambiguated the relative timing and localization of IC sensitivity with respect to that for grating orientation. Millisecond-by-millisecond analyses of VEPs and distributed source estimations revealed a main effect of grating orientation beginning at 65 ms post-stimulus onset within the calcarine sulcus that was followed by a main effect of IC presence beginning at 85 ms post-stimulus onset within the LOC. There was no evidence for differential processing of ICs as a function of the orientation of the grating. These results support models wherein IC sensitivity occurs first within the LOC.

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In order to interact with the multisensory world that surrounds us, we must integrate various sources of sensory information (vision, hearing, touch...). A fundamental question is thus how the brain integrates the separate elements of an object defined by several sensory components to form a unified percept. The superior colliculus was the main model for studying multisensory integration. At the cortical level, until recently, multisensory integration appeared to be a characteristic attributed to high-level association regions. First, we describe recently observed direct cortico-cortical connections between different sensory cortical areas in the non-human primate and discuss the potential role of these connections. Then, we show that the projections between different sensory and motor cortical areas and the thalamus enabled us to highlight the existence of thalamic nuclei that, by their connections, may represent an alternative pathway for information transfer between different sensory and/or motor cortical areas. The thalamus is in position to allow a faster transfer and even an integration of information across modalities. Finally, we discuss the role of these non-specific connections regarding behavioral evidence in the monkey and recent electrophysiological evidence in the primary cortical sensory areas.

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Does a conflict between inborn motor preferences and educational standards during childhood impact the structure of the adult human brain? To examine this issue, we acquired high-resolution T1-weighted magnetic resonance scans of the whole brain in adult "converted" left-handers who had been forced as children to become dextral writers. Analysis of sulcal surfaces revealed that consistent right- and left-handers showed an interhemispheric asymmetry in the surface area of the central sulcus with a greater surface contralateral to the dominant hand. This pattern was reversed in the converted group who showed a larger surface of the central sulcus in their left, nondominant hemisphere, indicating plasticity of the primary sensorimotor cortex caused by forced use of the nondominant hand. Voxel-based morphometry showed a reduction of gray matter volume in the middle part of the left putamen in converted left-handers relative to both consistently handed groups. A similar trend was found in the right putamen. Converted subjects with at least one left-handed first-degree relative showed a correlation between the acquired right-hand advantage for writing and the structural changes in putamen and pericentral cortex. Our results show that a specific environmental challenge during childhood can shape the macroscopic structure of the human basal ganglia. The smaller than normal putaminal volume differs markedly from previously reported enlargement of cortical gray matter associated with skill acquisition. This indicates a differential response of the basal ganglia to early environmental challenges, possibly related to processes of pruning during motor development.

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Introduction: Survival of children born prematurely or with very low birth weight has increased dramatically, but the long term developmental outcome remains unknown. Many children have deficits in cognitive capacities, in particular involving executive domains and those disabilities are likely to involve a central nervous system deficit. To understand their neurostructural origin, we use DTI. Structurally segregated and functionally regions of the cerebral cortex are interconnected by a dense network of axonal pathways. We noninvasively map these pathways across cortical hemispheres and construct normalized structural connection matrices derived from DTI MR tractography. Group comparisons of brain connectivity reveal significant changes in fiber density in case of children with poor intrauterine grown and extremely premature children (gestational age<28 weeks at birth) compared to control subjects. This changes suggest a link between cortico-axonal pathways and the central nervous system deficit. Methods: Sixty premature born infants (5-6 years old) were scanned on clinical 3T scanner (Magnetom Trio, Siemens Medical Solutions, Erlangen, Germany) at two hospitals (HUG, Geneva and CHUV, Lausanne). For each subject, T1-weighted MPRAGE images (TR/TE=2500/2.91,TI=1100, resolution=1x1x1mm, matrix=256x154) and DTI images (30 directions, TR/TE=10200/107, in-plane resolution=1.8x1.8x2mm, 64 axial, matrix=112x112) were acquired. Parent(s) provided written consent on prior ethical board approval. The extraction of the Whole Brain Structural Connectivity Matrix was performed following (Cammoun, 2009 and Hagmann, 2008). The MPARGE images were registered using an affine registration to the non-weighted-DTI and WM-GM segmentation performed on it. In order to have equal anatomical localization among subjects, 66 cortical regions with anatomical landmarks were created using the curvature information, i.e. sulcus and gyrus (Cammoun et al, 2007; Fischl et al, 2004; Desikan et al, 2006) with freesurfer software (http://surfer.nmr.mgh.harvard.edu/). Tractography was performed in WM using an algorithm especially designed for DTI/DSI data (Hagmann et al., 2007) and both information were then combined in a matrix. Each row and column of the matrix corresponds to a particular ROI. Each cell of index (i,j) represents the fiber density of the bundle connecting the ROIs i and j. Subdividing each cortical region, we obtained 4 Connectivity Matrices of different resolution (33, 66, 125 and 250 ROI/hemisphere) for each subject . Subjects were sorted in 3 different groups, namely (1) control, (2) Intrauterine Growth Restriction (IUGR), (3) Extreme Prematurity (EP), depending on their gestational age, weight and percentile-weight score at birth. Group-to-group comparisons were performed between groups (1)-(2) and (1)-(3). The mean age at examination of the three groups were similar. Results: Quantitative analysis were performed between groups to determine fibers density differences. For each group, a mean connectivity matrix with 33ROI/hemisphere resolution was computed. On the other hand, for all matrix resolutions (33,66,125,250 ROI/hemisphere), the number of bundles were computed and averaged. As seen in figure 1, EP and IUGR subjects present an overall reduction of fibers density in both interhemispherical and intrahemispherical connections. This is given quantitatively in table 1. IUGR subjects presents a higher percentage of missing fiber bundles than EP when compared to control subjects (~16% against 11%). When comparing both groups to control subjects, for the EP subjects, the occipito-parietal regions seem less interhemispherically connected whilst the intrahemispherical networks present lack of fiber density in the lymbic system. Children born with IUGR, have similar reductions in interhemispherical connections than the EP. However, the cuneus and precuneus connections with the precentral and paracentral lobe are even lower than in the case of the EP. For the intrahemispherical connections the IUGR group preset a loss of fiber density between the deep gray matter structures (striatum) and the frontal and middlefrontal poles, connections typically involved in the control of executive functions. For the qualitative analysis, a t-test comparing number of bundles (p-value<0.05) gave some preliminary significant results (figure 2). Again, even if both IUGR and EP appear to have significantly less connections comparing to the control subjects, the IUGR cohort seems to present a higher lack of fiber density specially relying the cuneus, precuneus and parietal areas. In terms of fiber density, preliminary Wilcoxon tests seem to validate the hypothesis set by the previous analysis. Conclusions: The goal of this study was to determine the effect of extreme prematurity and poor intrauterine growth on neurostructural development at the age of 6 years-old. This data indicates that differences in connectivity may well be the basis for the neurostructural and neuropsychological deficit described in these populations in the absence of overt brain lesions (Inder TE, 2005; Borradori-Tolsa, 2004; Dubois, 2008). Indeed, we suggest that IUGR and prematurity leads to alteration of connectivity between brain structures, especially in occipito-parietal and frontal lobes for EP and frontal and middletemporal poles for IUGR. Overall, IUGR children have a higher loss of connectivity in the overall connectivity matrix than EP children. In both cases, the localized alteration of connectivity suggests a direct link between cortico-axonal pathways and the central nervous system deficit. Our next step is to link these connectivity alterations to the performance in executive function tests.

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INTRODUCTION: Handwriting is a modality of language production whose cerebral substrates remain poorly known although the existence of specific regions is postulated. The description of brain damaged patients with agraphia and, more recently, several neuroimaging studies suggest the involvement of different brain regions. However, results vary with the methodological choices made and may not always discriminate between "writing-specific" and motor or linguistic processes shared with other abilities. METHODS: We used the "Activation Likelihood Estimate" (ALE) meta-analytical method to identify the cerebral network of areas commonly activated during handwriting in 18 neuroimaging studies published in the literature. Included contrasts were also classified according to the control tasks used, whether non-specific motor/output-control or linguistic/input-control. These data were included in two secondary meta-analyses in order to reveal the functional role of the different areas of this network. RESULTS: An extensive, mainly left-hemisphere network of 12 cortical and sub-cortical areas was obtained; three of which were considered as primarily writing-specific (left superior frontal sulcus/middle frontal gyrus area, left intraparietal sulcus/superior parietal area, right cerebellum) while others related rather to non-specific motor (primary motor and sensorimotor cortex, supplementary motor area, thalamus and putamen) or linguistic processes (ventral premotor cortex, posterior/inferior temporal cortex). CONCLUSIONS: This meta-analysis provides a description of the cerebral network of handwriting as revealed by various types of neuroimaging experiments and confirms the crucial involvement of the left frontal and superior parietal regions. These findings provide new insights into cognitive processes involved in handwriting and their cerebral substrates.