Biomedical subjects

Ingrid Johnsrude

Publications and source records attributed to Ingrid Johnsrude.

3 recordsLinked to original sources

PubMed2005

Interleaved silent steady state (ISSS) imaging: a new sparse imaging method applied to auditory fMRI.

The acoustic scanner noise that is generated by rapid gradient switching in echo planar imaging (EPI) is an important confounding factor in auditory fMRI. "Sparse imaging" designs overcome the influence of scanner noise on stimulus presentation by acquiring single brain volumes following a silent stimulus presentation period. However, conventional sparse imaging requires assumptions about the time-to-peak of the evoked hemodynamic response and reduces the amount of EPI data which can be acquired and hence statistical power. In this article, we describe an "interleaved silent steady state" (ISSS) sampling scheme in which we rapidly acquire a set of EPI volumes following each silent stimulus presentation period. We avoid T1-related signal decay during the acquisition of the EPI volumes by maintaining the steady state longitudinal magnetization with a train of silent slice-selective excitation pulses during the silent period, ensuring that signal contrast is constant across successive scans. A validation study comparing ISSS to conventional sparse imaging demonstrates that ISSS imaging provides time course information that is absent in conventional sparse imaging data. The ISSS sequence has a temporal resolution like event-related (ER) imaging within a single trial (unlike conventional sparse imaging, where ER-like temporal resolution can only be achieved by compiling data across many jittered trials of the same stimulus type). This temporal resolution within trials makes ISSS particularly suitable for experiments in which a) scanner noise would interfere with the perception and processing of the stimulus; b) stimuli are several seconds in duration, and activation is expected to evolve and change as the stimulus unfolds; and c) it is impractical to present a single stimulus more than once (for example, repetition priming or familiarity effects would be expected).

Acoustic Stimulation↗

PubMed2004

Somatotopic representation of action words in human motor and premotor cortex.

Since the early days of research into language and the brain, word meaning was assumed to be processed in specific brain regions, which most modern neuroscientists localize to the left temporal lobe. Here we use event-related fMRI to show that action words referring to face, arm, or leg actions (e.g., to lick, pick, or kick), when presented in a passive reading task, differentially activated areas along the motor strip that either were directly adjacent to or overlapped with areas activated by actual movement of the tongue, fingers, or feet. These results demonstrate that the referential meaning of action words has a correlate in the somatotopic activation of motor and premotor cortex. This rules out a unified "meaning center" in the human brain and supports a dynamic view according to which words are processed by distributed neuronal assemblies with cortical topographies that reflect word semantics.

Adult↗

PubMed2004

Cognitive tasks for driving a brain-computer interfacing system: a pilot study.

Different cognitive tasks were investigated for use with a brain-computer interface (BCI). The main aim was to evaluate which two of several candidate tasks lead to patterns of electroencephalographic (EEG) activity that could be differentiated most reliably and, therefore, produce the highest communication rate. An optimal signal processing method was also sought to enhance differentiation of EEG profiles across tasks. In ten normal subjects (five male), aged 29-54 years, EEG activity was recorded from four channels during cognitive tasks grouped in pairs, and performed alternately. Four imagery tasks were: spatial navigation around a familiar environment; auditory imagery of a familiar tune; and right and left motor imagery of opening and closing the hand. Signal processing methodology included autoregressive (AR) modeling and classification based on logistic regression and a nonlinear generative classifier. The highest communication rate was found using the navigation and auditory imagery tasks. In terms of classification performance and, hence, possible communication rate, these results were significantly better (p < 0.05) than those obtained with the classical pairing of motor tasks involving imaginary movements of the left and right hands. In terms of EEG data analysis, a nonlinear classification model provided more robust results than a linear model (p << 0.01), and a lower AR model order than those used in previous work was found to be effective. These findings have implications for establishing appropriate methods to operate BCI systems, particularly for disabled people who may experience difficulty with motor tasks, even motor imagery.

Adult↗