Showing papers by "Satrajit S. Ghosh published in 2011"

Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python

Abstract: Current neuroimaging software offer users an incredible opportunity to analyze their data in different ways, with different underlying assumptions. Several sophisticated software packages (e.g., AFNI, BrainVoyager, FSL, FreeSurfer, Nipy, R, SPM) are used to process and analyze large and often diverse (highly multi-dimensional) data. However, this heterogeneous collection of specialized applications creates several issues that hinder replicable, efficient and optimal use of neuroimaging analysis approaches: 1) No uniform access to neuroimaging analysis software and usage information; 2) No framework for comparative algorithm development and dissemination; 3) Personnel turnover in laboratories often limits methodological continuity and training new personnel takes time; 4) Neuroimaging software packages do not address computational efficiency; and 5) Methods sections in journal articles are inadequate for reproducing results. To address these issues, we present Nipype (Neuroimaging in Python: Pipelines and Interfaces; http://nipy.org/nipype), an open-source, community-developed, software package and scriptable library. Nipype solves the issues by providing Interfaces to existing neuroimaging software with uniform usage semantics and by facilitating interaction between these packages using Workflows. Nipype provides an environment that encourages interactive exploration of algorithms, eases the design of Workflows within and between packages, allows rapid comparative development of algorithms and reduces the learning curve necessary to use different packages. Nipype supports both local and remote execution on multi-core machines and clusters, without additional scripting. Nipype is BSD licensed, allowing anyone unrestricted usage. An open, community-driven development philosophy allows the software to quickly adapt and address the varied needs of the evolving neuroimaging community, especially in the context of increasing demand for reproducible research.

Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python

Abstract: Current neuroimaging software offer users an incredible opportunity to analyze their data in different ways, with different underlying assumptions. Several sophisticated software packages (e.g., AFNI, BrainVoyager, FSL, FreeSurfer, Nipy, R, SPM) are used to process and analyze large and often diverse (highly multi-dimensional) data. However, this heterogeneous collection of specialized applications creates several issues that hinder replicable, efficient and optimal use of neuroimaging analysis approaches: 1) No uniform access to neuroimaging analysis software and usage information; 2) No framework for comparative algorithm development and dissemination; 3) Personnel turnover in laboratories often limits methodological continuity and training new personnel takes time; 4) Neuroimaging software packages do not address computational efficiency; and 5) Methods sections in journal articles are inadequate for reproducing results. To address these issues, we present Nipype (Neuroimaging in Python: Pipelines and Interfaces; http://nipy.org/nipype), an open-source, community-developed, software package and scriptable library. Nipype solves the issues by providing Interfaces to existing neuroimaging software with uniform usage semantics and by facilitating interaction between these packages using Workflows. Nipype provides an environment that encourages interactive exploration of algorithms, eases the design of Workflows within and between packages, allows rapid comparative development of algorithms and reduces the learning curve necessary to use different packages. Nipype supports both local and remote execution on multi-core machines and clusters, without additional scripting. Nipype is BSD licensed, allowing anyone unrestricted usage. An open, community-driven development philosophy allows the software to quickly adapt and address the varied needs of the evolving neuroimaging community, especially in the context of increasing demand for reproducible research.

Focal Manipulations of Formant Trajectories Reveal a Role of Auditory Feedback in the Online Control of Both Within-Syllable and Between-Syllable Speech Timing

Abstract: Within the human motor repertoire, speech production has a uniquely high level of spatiotemporal complexity. The production of running speech comprises the traversing of spatial positions with precisely coordinated articulator movements to produce 10-15 sounds/s. How does the brain use auditory feedback, namely the self-perception of produced speech sounds, in the online control of spatial and temporal parameters of multisyllabic articulation? This question has important bearings on the organizational principles of sequential actions, yet its answer remains controversial due to the long latency of the auditory feedback pathway and technical challenges involved in manipulating auditory feedback in precisely controlled ways during running speech. In this study, we developed a novel technique for introducing time-varying, focal perturbations in the auditory feedback during multisyllabic, connected speech. Manipulations of spatial and temporal parameters of the formant trajectory were tested separately on two groups of subjects as they uttered "I owe you a yo-yo." Under these perturbations, significant and specific changes were observed in both the spatial and temporal parameters of the produced formant trajectories. Compensations to spatial perturbations were bidirectional and opposed the perturbations. Furthermore, under perturbations that manipulated the timing of auditory feedback trajectory (slow-down or speed-up), significant adjustments in syllable timing were observed in the subjects' productions. These results highlight the systematic roles of auditory feedback in the online control of a highly over-learned action as connected speech articulation and provide a first look at the properties of this type of sensorimotor interaction in sequential movements.

Cine Magnetic Resonance Imaging With Simultaneous Audio to Evaluate Pediatric Velopharyngeal Insufficiency

Abstract: Objective To develop a protocol linking cine magnetic resonance (MR) imaging to simultaneously acquired audio recordings of specific phonatory tasks to evaluate velopharyngeal insufficiency (VPI) in children. Design Institutional review board–approved development and application of a novel dynamic cine MR imaging protocol linked to simultaneously recorded audio. Setting A tertiary care multidisciplinary pediatric airway center. Participants Three healthy adult volunteers and 5 pediatric volunteers (age range, 9.3-18.9 years; mean age, 12.4 years) from the multidisciplinary pediatric airway center with VPI who previously had undergone nasopharyngoscopy, videofluoroscopy, or both. Interventions Cine MR imaging with simultaneously acquired audio files was performed in 3 adult volunteers to optimize the protocol and then in 5 pediatric volunteers meeting the inclusion criteria. Main Outcome Measures High-resolution cine MR images with clear intelligible audio recordings of specific phonatory tasks. Results Using 3 healthy adult volunteers, a cine MR imaging VPI protocol was developed that links simultaneously acquired cine MR images to audio recordings of specific validated phonatory tasks. Five school-aged children with VPI from our multidisciplinary pediatric airway center were then enrolled and underwent cine MR imaging using this protocol. The cine MR images and audio recordings acquired were of sufficient diagnostic quality to evaluate VPI closure patterns in school-aged children with VPI. Conclusion Cine MR imaging linked to audio is a quick, safe, and well-tolerated dynamic diagnostic imaging tool that may eventually have the potential to guide more precisely the selection and application of surgical techniques for VPI.

The influence of auditory acuity on acoustic variability and the use of motor equivalence during adaptation to a perturbation.

Abstract: Purpose The aim of this study was to relate speakers' auditory acuity for the sibilant contrast, their use of motor equivalent trading relationships in producing the sibilant /ʃ/, and their produce...

Phonetic Variability in Speech Perception and the Phonological Deficit in Dyslexia.

Abstract: Accounting for phonetic variability across talkers is a core challenge in speech perception. Cognitive models of word recognition address variability by employing either episodic or talker-normalization based approaches. In developmental dyslexia, a “phonological deficit” is thought to impair the acquisition of typical reading ability; however, no connection has yet been made between such a phonological deficit and speech perception behavior, which appears intact in dyslexia. We demonstrate differences between dyslexic listeners and controls in two tasks involving processing phonetic variability: First, dyslexics exhibited impaired talker identification abilities in a familiar language but not a foreign one. Second, the brains of individuals with dyslexia exhibited reduced hemodynamic adaptation – a neural index of talker normalization – compared to typical readers. Together, these results suggest speech perception in dyslexia relies primarily on episodic processes, and that the inability to fully utilize talker-normalization processes may impair the formation of the abstract representations of speech sounds necessary for robust sound-to-letter mapping during reading development.

Optimized design and analysis of sparse‐sampling functional magnetic resonance imaging experiments of speech and hearing.

Abstract: Functional magnetic resonance imaging (fMRI) offers an unparalleled opportunity to investigate the brain bases of speech and hearing. However, the high‐amplitude (>90 dB) acoustic noise that occurs during MR image acquisition presents a serious obstacle to research on speech perception and production. “Sparse sampling” is an alternate acquisition strategy that mitigates the interference of this acoustic noise by inserting a delay between subsequent image acquisitions, allowing auditory stimulus presentation or speech production during this silent period. Although this technique is routinely employed in auditory fMRI, there has been no empirical attempt to optimize the design of sparse sampling paradigms to maximize detection of whole‐brain blood‐oxygen level dependent (BOLD) signal. Moreover, the discontinuous nature of the sparse‐sampling timeseries has led to the use of analysis models that fail to account for dynamic properties of the hemodynamic response: thus seriously underestimating BOLD signal and...