Increased Cognitive Control During Execution of Finger Tap Movement in People with Parkinson's Disease.

BACKGROUND: Previous studies employed demanding and complex hand tasks to study the brain activation in people with Parkinson's Disease (PD). There is inconsistent finding about the cerebellar activity during movement execution of this patient population. OBJECTIVES: This study aimed to examine the brain activation patterns of PD individuals in the on-state and healthy control subjects in a simple finger tapping task. METHODS: Twenty-seven patients with PD and 22 age-matched healthy subjects were recruited for the study. Subjects were instructed to perform simple finger tapping tasks under self- and cue-initiated conditions in separate runs while their brain activations were captured using fMRI. RESULTS: Healthy subjects had higher brain activity in contralateral precentral gyrus during the self-initiated task, and higher brain activity in the ipsilateral middle occipital gyrus during the cue-initiated task. PD patients had higher brain activity in the cerebellum Crus I (bilateral) and lobules VI (ipsilateral) during the self-initiated task and higher brain activity in the contralateral middle frontal gyrus during the cue-initiated task. When compared with healthy controls, PD patients had lower brain activity in the contralateral inferior parietal lobule during the self-initiated task, and lower brain activity in the ipsilateral cerebellum lobule VIII, lobule VIIB and vermis VIII, and thalamus during the cue-initiated task. Conjunction analysis indicated that both groups had activation in bilateral cerebellum and SMA and ipsilateral precentral gyrus and postcentral gyrus during both self- and cue-initiated movement. Individuals with PD exhibited higher brain activity in the executive zone (cerebellum Crus I and II) during self-initiated movement, and lower brain activity in the sensorimotor zone (i.e. lobule VIIb and VIII of the cerebellum) during cue-initiated movement. DISCUSSIONS: The findings suggest that individuals with PD may use more executive control when performing simple movements.

Importance of punishment frequency in the Iowa gambling task: an fMRI study.

It has been widely found that in the Iowa Gambling Task (IGT; Bechara et al. Cognition, 50(1), 7-15 1994) normal subjects would gradually learn to prefer obtaining rewards for long-term benefits than seeking immediate rewards to maximize the overall profit. The current study aimed to gain an understanding of how punishment frequency in the IGT would be processed and its association with subjects' reward preferences. In this study, we employed the clinical version of the IGT, in which response options are not only different in the long-term outcome, but also associated with different punishment frequencies. Event-related functional Magnetic Resonance Imaging (fMRI) was used to capture the subjects' brain activity when performing the IGT. A total of 24 male subjects (mean age = 21.7 years, SD = 1.8 years), who were university students, participated in the experiment. It is found that subjects learned to select more from the decks that were advantageous in the long-term, but they were more sensitive to the effect of long-term outcome under the condition of high punishment frequency. The corresponding brain activation showed that the Anterior Cingulate Cortex (ACC) had significantly higher activation during the disadvantageous choices than the advantageous choices. Such activity difference between the two conditions of long-term outcome was more prominent with high punishment frequency than low punishment frequency; and this brain activity difference was significantly correlated with the behavioral performance under the condition of high punishment frequency. The results suggested that only in the context with high punishment frequency, there would be increased neural activity in ACC when subjects intended to select from the disadvantageous choices so that these choices would be inhibited and advantageous choices would be selected.

Is resting-state functional connectivity revealed by functional near-infrared spectroscopy test-retest reliable?

Recently, resting-state functional near-infrared spectroscopy (rs-fNIRS) research has experienced tremendous progress. Resting-state functional connectivity (RSFC) has been adopted as a pivotal biomarker in rs-fNIRS studies. However, it is yet to be clear if the RSFC derived from rs-fNIRS is reliable. This concern impedes extensive utilization of rs-fNIRS. We systematically address the issue of reliability. Sixteen subjects participate in two rs-fNIRS sessions held one week apart. RSFC in sensorimotor system is calculated using the seed-correlation approach. Then, test-retest reliability is evaluated at three different scales (map-, cluster-, and channelwise) for individual- and group-level RSFC derived from different types of fNIRS signals [oxygenated (HbO), deoxygenated (HbR), and total hemoglobin (HbT)]. The results show that, for HbO signals, individual-level RSFC generally has good-to-excellent map-/clusterwise reliability, while group-level RSFC has excellent reliability. For HbT signals, the results are similar. For HbR signals, the clusterwise reliability is comparable to that for HbO while the mapwise reliability is slightly lower (fair to good). Focusing on RSFC at a single channel, we report poor channelwise reliability for all three types of signals. We hereby propose that fNIRS-derived RSFC is a reliable biomarker if interpreted in map- and clusterwise manners. However, channelwise interpretation of individual RSFC should proceed with caution.

Subject order-independent group ICA (SOI-GICA) for functional MRI data analysis.

Independent component analysis (ICA) is a data-driven approach to study functional magnetic resonance imaging (fMRI) data. Particularly, for group analysis on multiple subjects, temporally concatenation group ICA (TC-GICA) is intensively used. However, due to the usually limited computational capability, data reduction with principal component analysis (PCA: a standard preprocessing step of ICA decomposition) is difficult to achieve for a large dataset. To overcome this, TC-GICA employs multiple-stage PCA data reduction. Such multiple-stage PCA data reduction, however, leads to variable outputs due to different subject concatenation orders. Consequently, the ICA algorithm uses the variable multiple-stage PCA outputs and generates variable decompositions. In this study, a rigorous theoretical analysis was conducted to prove the existence of such variability. Simulated and real fMRI experiments were used to demonstrate the subject-order-induced variability of TC-GICA results using multiple PCA data reductions. To solve this problem, we propose a new subject order-independent group ICA (SOI-GICA). Both simulated and real fMRI data experiments demonstrated the high robustness and accuracy of the SOI-GICA results compared to those of traditional TC-GICA. Accordingly, we recommend SOI-GICA for group ICA-based fMRI studies, especially those with large data sets.

Functional connectivity as revealed by independent component analysis of resting-state fNIRS measurements.

As a promising non-invasive imaging technique, functional near infrared spectroscopy (fNIRS) has recently earned increasing attention in resting-state functional connectivity (RSFC) studies. Preliminary fNIRS-based RSFC studies adopted a seed correlation approach and yielded interesting results. However, the seed correlation approach has several inherent problems, such as neglecting of interactions among multiple regions and a dependence on seed region selection. Moreover, ineffectively reduced noise and artifacts in fNIRS measurements also negatively affect RSFC results. In this study, independent component analysis (ICA) was introduced to meet these challenges in RSFC detection based on resting-state fNIRS measurements. The results of ICA on data from the sensorimotor and the visual systems both showed functional system-specific RSFC maps. Results from comparison between ICA and the conventional seed correlation approach demonstrated, both qualitatively and quantitatively, the superior performance of ICA with higher sensitivity and specificity, especially in the case of higher noise level. The capability of ICA to separate noise and artifacts from resting-state fNIRS data was also demonstrated, and the extracted noise and artifacts were illustrated. Finally, some practical issues on performing ICA on resting-state fNIRS data were discussed.