Current Issue : October - December Volume : 2013 Issue Number : 4 Articles : 5 Articles
Background: We report on a 6-year-old Turkish boy with profound sensorineural deafness, balance disorder, severe\r\ndisorder of oral motor function, and mild developmental delay. Further findings included scaphocephaly,\r\nplagiocephaly, long palpebral fissures, high narrow palate, low-set posteriorly rotated ears, torticollis, hypoplastic\r\ngenitalia and faulty foot posture. Parents were consanguineous.\r\nMethods and results: Computed tomography and magnetic resonance imaging showed bilateral single widened\r\ncochlear turn, narrowing of the internal auditory canal, and bilateral truncation of the vestibulo-cochlear nerve.\r\nMicroarray analysis and next generation sequencing showed a homozygous deletion of chromosome 5q31.1\r\nspanning 115.3 kb and including three genes: NEUROG1 (encoding neurogenin 1), DCNP1 (dendritic cell nuclear\r\nprotein 1, C5ORF20) and TIFAB (TIFA-related protein). The inability to chew and swallow, deafness and balance\r\ndisorder represented congenital palsies of cranial nerves V (trigeminal nerve) and VIII (vestibulo-cochlear nerve) and\r\nthus a congenital cranial dysinnervation disorder.\r\nConclusions: Based on reported phenotypes of neurog1 null mutant mice and other vertebrates, we strongly\r\npropose NEUROG1 as the causative gene in this boy. The human NEUROG1 resides within the DFNB60 locus for\r\nnon-syndromic autosomal recessive deafness on chromosome 5q22-q31, but linkage data have excluded it from\r\nbeing causative in the DFNB60 patients. Given its large size (35 Mb, >100 genes), the 5q22-q31 area could harbor\r\nmore than one deafness gene. We propose NEUROG1 as a new gene for syndromic autosomal recessive hearing\r\nloss and congenital cranial dysinnervation disorder including cranial nerves V and VIII....
Background: Recent data support the beneficial role of gesturing during mental practice. The present study\r\nexamined whether coupling motor imagery (MI) with some movement sequences (dynamic imagery condition)\r\nimpacted motor performance to a greater extent than performing MI while remaining motionless.\r\nMethods: A group of active high jumpers imagined their jump both with and without associated arm movement.\r\nThree outcome variables were measured: the number of successful attempts, the temporal congruence between MI\r\nand actual jump performance, and the technical quality of the jump.\r\nResults: Data revealed that dynamic imagery enhanced both MI quality and temporal congruence between MI and\r\nmotor performance, and further improved the technical efficacy of the jump. Athletes also reported more vivid\r\nrepresentation while coupling MI with actual movement.\r\nConclusions: These data support the hypothesis that performing dynamic imagery might contribute to enhance\r\nMI quality and efficacy, and sketch potentially fruitful new directions for MI practic...
The treatment of cerebral cavernous angioma\r\n(CCA) has caused great controversy. In the case of\r\nrecurrent haemorrhage, frequent epileptic attacks, clear\r\ndysneuria, etc., and by taking into consideration focal\r\npositions, number of foci, and conditions conducive to\r\noperations, it is our opinion that excisionofCCAfoci is\r\nthefirstchoicetocurethedisease.Controversyregarding\r\nthegammaknifetreatmentofCCAhasexistedforalong\r\ntime. The main reason behind this is that analyses and\r\nappraisals of its therapeutic effect are at variance, for\r\nexample in terms of incidence rate of recurrent\r\nhaemorrhage, control over epilepsy, comparison of\r\ndysneuria before and after treatment, incidence of\r\nhydrocephalus and cerebral necrosis, focal volume,\r\npathologicalchangesoffociafterthetreatment,etc....
Background: Complex movement sequences are composed of segments with different levels of functionality:\r\nintended segments towards a goal and segments that spontaneously occur largely beneath our awareness. It is not\r\nknown if these spontaneously-occurring segments could be informative of the learning progression in na�¯ve\r\nsubjects trying to skillfully master a new sport routine.\r\nMethods: To address this question we asked if the hand speed variability could be modeled as a stochastic process\r\nwhere each trial speed depended on the speed of the previous trial. We specifically asked if the hand speed\r\nmaximum from a previous trial could accurately predict the maximum speed of a sub-sequent trial in both\r\nintended and spontaneous movement segments. We further asked whether experts and novices manifested similar\r\nmodels, despite different kinematic dynamics and assessed the predictive power of the spontaneous fluctuations in\r\nthe incidental motions.\r\nResults: We found a simple power rule to parameterize speed variability for expert and novices with accurate\r\npredictive value despite randomly instructed speed levels and training contexts. This rule on average tended to\r\nyield similar exponent across speed levels for intended motion segments. Yet for the spontaneous segments the\r\nspeed fluctuations had exponents that changed as a function of speed level and training context. Two conditions\r\nhighlighted the expert performance: broad bandwidth of velocity-dependent parameter values and low noise-tosignal\r\nratios that unambiguously distinguished between training regimes. Neither of these was yet manifested in\r\nthe novices.\r\nConclusions: We suggest that the statistics of intended motions may be a predictor of overall expertise level,\r\nwhereas those of spontaneously occurring incidental motions may serve to track learning progression in different\r\ntraining contexts. These spontaneous fluctuations may help the central systems to kinesthetically discriminate the\r\nperipheral re-afferent patterns of movement variability associated with changes in movement speed and training\r\ncontext. We further propose that during learning the acquisition of both broad bandwidth of speeds and low\r\nnoise-to-signal ratios may be critical to build a verifiable kinesthetic (movement) percept and reach the type of\r\nautomaticity that an expert acquires....
Parkinson�s disease (PD), the second most\r\ncommon neurodegenerative disease, is characterized by\r\nthe progressive loss of dopaminergic neurons in the\r\nsubstantianigraparscompacta(SNpc)andtheformation\r\nof intracytoplasmic Lewy inclusion bodies. To date, the\r\ndiagnosisofidiopathicPDismainlybasedonitscardinal\r\nclinical features: resting tremor, bradykinesia and\r\nrigidity.Inrecentyears,advancesinmagneticresonance\r\nimaging (MRI), transcranial sonography (TCS) and\r\nfunctional imaging which includes positron emission\r\ntomographyPETand single photon emission\r\ncomputed tomography (SPECT) have provided new\r\ntoolsforthediagnosisofPDinitsearlystagesandhave\r\ndiscriminated it from other atypical Parkinsonian\r\nsyndromes. This review focuses mainly on the current\r\ndevelopment of neuroimaging and its application in the\r\ndiagnosisanddifferentialdiagnosisofPD....
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