Neural systems for speech and song in autism
نویسندگان
چکیده
Despite language disabilities in autism, music abilities are frequently preserved. Paradoxically, brain regions associated with these functions typically overlap, enabling investigation of neural organization supporting speech and song in autism. Neural systems sensitive to speech and song were compared in low-functioning autistic and age-matched control children using passive auditory stimulation during functional magnetic resonance and diffusion tensor imaging. Activation in left inferior frontal gyrus was reduced in autistic children relative to controls during speech stimulation, but was greater than controls during song stimulation. Functional connectivity for song relative to speech was also increased between left inferior frontal gyrus and superior temporal gyrus in autism, and large-scale connectivity showed increased frontal-posterior connections. Although fractional anisotropy of the left arcuate fasciculus was decreased in autistic children relative to controls, structural terminations of the arcuate fasciculus in inferior frontal gyrus were indistinguishable between autistic and control groups. Fractional anisotropy correlated with activity in left inferior frontal gyrus for both speech and song conditions. Together, these findings indicate that in autism, functional systems that process speech and song were more effectively engaged for song than for speech and projections of structural pathways associated with these functions were not distinguishable from controls.
منابع مشابه
Persian Phone Recognition Using Acoustic Landmarks and Neural Network-based variability compensation methods
Speech recognition is a subfield of artificial intelligence that develops technologies to convert speech utterance into transcription. So far, various methods such as hidden Markov models and artificial neural networks have been used to develop speech recognition systems. In most of these systems, the speech signal frames are processed uniformly, while the information is not evenly distributed ...
متن کاملروشی جدید در بازشناسی مقاوم گفتار مبتنی بر دادگان مفقود با استفاده از شبکه عصبی دوسویه
Performance of speech recognition systems is greatly reduced when speech corrupted by noise. One common method for robust speech recognition systems is missing feature methods. In this way, the components in time - frequency representation of signal (Spectrogram) that present low signal to noise ratio (SNR), are tagged as missing and deleted then replaced by remained components and statistical ...
متن کاملشبکه عصبی پیچشی با پنجرههای قابل تطبیق برای بازشناسی گفتار
Although, speech recognition systems are widely used and their accuracies are continuously increased, there is a considerable performance gap between their accuracies and human recognition ability. This is partially due to high speaker variations in speech signal. Deep neural networks are among the best tools for acoustic modeling. Recently, using hybrid deep neural network and hidden Markov mo...
متن کاملClassification of Iranian traditional musical modes (DASTGÄH) with artificial neural network
The concept of Iranian traditional musical modes, namely DASTGÄH, is the basis for the traditional music system. The concept introduces seven DASTGÄHs. It is not an easy process to distinguish these modes and such practice is commonly performed by an experienced person in this field. Apparently, applying artificial intelligence to do such classification requires a combination of the basic infor...
متن کاملArrhythmic Song Exposure Increases ZENK Expression in Auditory Cortical Areas and Nucleus Taeniae of the Adult Zebra Finch
Rhythm is important in the production of motor sequences such as speech and song. Deficits in rhythm processing have been implicated in human disorders that affect speech and language processing, including stuttering, autism, and dyslexia. Songbirds provide a tractable model for studying the neural underpinnings of rhythm processing due to parallels with humans in neural structures and vocal le...
متن کامل