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- All Subjects: Machine Learning
- All Subjects: aphasia
- Creators: Berisha, Visar
The purpose of this study is to know whether the primary motor cortex (M1) plays a role in the sensorimotor memory. It was hypothesized that temporary disruption of the M1 following the learning to minimize a tilt using a ‘L’ shaped object would negatively affect the retention of sensorimotor memory and thus reduce interference between the memory acquired in one context and the visual cues to perform the same task in a different context.
Significant findings were shown in blocks 1, 2, and 4. In block 3, subjects displayed insignificant amount of learning. However, it cannot be concluded that there is full interference in block 3. Therefore, looked into 3 effects in statistical analysis: the main effects of the blocks, the main effects of the trials, and the effects of the blocks and trials combined. From the block effects, there is a p-value of 0.001, and from the trial effects, the p-value is less than 0.001. Both of these effects indicate that there is learning occurring. However, when looking at the blocks * trials effects, we see a p-value of 0.002 < 0.05 indicating significant interaction between sensorimotor memories. Based on the results that were found, there is a presence of interference in all the blocks but not enough to justify the use of TMS in order to reduce interference because there is a partial reduction of interference from the control experiment. It is evident that the time delay might be the issue between context switches. By reducing the time delay between block 2 and 3 from 10 minutes to 5 minutes, I will hope to see significant learning to occur from the first trial to the second trial.
The distinctions between the neural resources supporting speech and music comprehension have long been studied using contexts like aphasia and amusia, and neuroimaging in control subjects. While many models have emerged to describe the different networks uniquely recruited in response to speech and music stimuli, there are still many questions, especially regarding left-hemispheric strokes that disrupt typical speech-processing brain networks, and how musical training might affect the brain networks recruited for speech after a stroke. Thus, our study aims to explore some questions related to the above topics. We collected task-based functional MRI data from 12 subjects who previously experienced a left-hemispheric stroke. Subjects listened to blocks of spoken sentences and novel piano melodies during scanning to examine the differences in brain activations in response to speech and music. We hypothesized that speech stimuli would activate right frontal regions, and music stimuli would activate the right superior temporal regions more than speech (both findings not seen in previous studies of control subjects), as a result of functional changes in the brain, following the left-hemispheric stroke and particularly the loss of functionality in the left temporal lobe. We also hypothesized that the music stimuli would cause a stronger activation in right temporal cortex for participants who have had musical training than those who have not. Our results indicate that speech stimuli compared to rest activated the anterior superior temporal gyrus bilaterally and activated the right inferior frontal lobe. Music stimuli compared to rest did not activate the brain bilaterally, but rather only activated the right middle temporal gyrus. When the group analysis was performed with music experience as a covariate, we found that musical training did not affect activations to music stimuli specifically, but there was greater right hemisphere activation in several regions in response to speech stimuli as a function of more years of musical training. The results of the study agree with our hypotheses regarding the functional changes in the brain, but they conflict with our hypothesis about musical expertise. Overall, the study has generated interesting starting points for further explorations of how musical neural resources may be recruited for speech processing after damage to typical language networks.
Aphasia is an impairment that affects many different aspects of language and makes it more difficult for a person to communicate with those around them. Treatment for aphasia is often administered by a speech-language pathologist in a clinical setting, but researchers have recently begun exploring the potential of virtual reality (VR) interventions. VR provides an immersive environment and can allow multiple users to interact with digitized content. This exploratory paper proposes the design of a VR rehabilitation game –called Pact– for adults with aphasia that aims to improve the word-finding and picture-naming abilities of users to improve communication skills. Additionally, a study is proposed that will assess how well Pact improves the word-finding and picture-naming abilities of users when it is used in conjunction with speech therapy. If the results of the study show an increase in word-finding and picture-naming scores compared to the control group (patients receiving traditional speech therapy alone), the results would indicate that Pact can achieve its goal of promoting improvement in these domains. There is a further need to examine VR interventions for aphasia, particularly with larger sample sizes that explore the gains associated with or design issues associated with multi-user VR programs.
This paper serves to report the research performed towards detecting PD and the effects of medication through the use of machine learning and finger tapping data collected through mobile devices. The primary objective for this research is to prototype a PD classification model and a medication classification model that predict the following: the individual’s disease status and the medication intake time relative to performing the finger-tapping activity, respectively.
This paper serves to report the research performed towards detecting PD and the effects of medication through the use of machine learning and finger tapping data collected through mobile devices. The primary objective for this research is to prototype a PD classification model and a medication classification model that predict the following: the individual’s disease status and the medication intake time relative to performing the finger-tapping activity, respectively.
Aphasia is an acquired speech-language disorder that is brought upon because of post-stroke damage to the left hemisphere of the brain. Treatment for individuals with these speech production impairments can be challenging for clinicians because there is high variability in language recovery after stroke and lesion size does not predict language outcome (Lazar et al, 2008). It is also important to note that adequate integration between the sensory and motor systems is critical for many aspects of fluent speech and correcting speech errors. The present study seeks to investigate how delayed auditory-feedback paradigms, which alter the time scale of sensorimotor interactions in speech, might be useful in characterizing the speech production impairments in individuals with aphasia. To this end, six individuals with aphasia and nine age-matched control subjects were introduced to delayed auditory feedback at 4 different intervals during a sentence reading task. Our study found that the aphasia group generated more errors in 3 out of the 4 linguistic categories measured across all delay lengths, but that there was no significant main effect delay or interaction between group and delay. Acoustic analyses revealed variability among scores within the control and aphasia groups on all phoneme types. For example, acoustic analyses highlighted how the individual with conduction aphasia showed significantly longer amplitudes at all delays, and significantly larger duration at no delay, but that significance diminished as delay periods increased. Overall, this study suggests that delayed auditory feedback’s effects vary across individuals with aphasia and provides a base of research to be further built on by future testing of individuals with varying aphasia types and levels of severity.