Pre-surgical fMRI Localization of the Hand Motor Cortex in Brain Tumors: Comparison Between Finger Tapping Task and a New Visual-Triggered Finger Movement Task

Individual analysis of fMRI data reveals incongruency in a potential CADASIL biomarker

fMRI-based studies on neurodegenerative diseases rarely report single-subject information, which is useful for assessing potential biomarkers. In a previous fMRI study, CADASIL patients showed, at the group level, a significant reduction of the long-lasting visually stimulated hyperaemic response. Here, we used data interpolation and computed a hemodynamic response function from the 20-s visual response to achieve a 40-s response prediction at the individual level. The comparison between the expected and recorded 40-s responses confirmed the occurrence of a late and frequent response reduction among patients. However, this feature was inversely related to age and was also detected in control subjects, which suggests that this potential biomarker cannot be retained for monitoring vascular dysfunction in CADASIL. We showcase an open-source analytical pipeline for single-subject analysis to quickly assess potential biomarkers in fMRI studies.

The new morphologic classification of the hand motor cortex with magnetic resonance imaging in glioma patients

Purpose

The hand motor cortex (HMC) is a reliable anatomical landmark for identifying the precentral gyrus. The current study aimed to investigate the morphology of HMC on axial MRI of glioma patients, propose a new morphological classification of HMC and analyze the effect of tumors on the morphology of HMC.

Methods

A retrospective study of 276 adult right-handed glioma patients was conducted. The morphology of HMC was assessed using T2 axial images. Subsequently, the distribution of morphological subtypes was compared between the bilateral hemispheres and the tumor-affected and healthy hemispheres. Finally, the influence of tumor pathology on the morphology of HMC was investigated.

Results

A new morphological classification of HMC with four subtypes (Ω, ε, Ω-ε and ε-Ω) was proposed. No significant difference was identified in the distribution of morphological subtypes between the bilateral hemispheres (p = 0.0901, Chi-square test), or between the tumor-affected and healthy hemispheres (p = 0.3507, Chi-square test), and the morphology of HMC between the bilateral hemispheres were consistent (p < 0.0001, Kappa test). In addition, a significant difference was identified in the distribution of morphological subtypes between astrocytic and oligodendroglial tumors (p = 0.0135, Chi-square test).

Conclusion

In the current study, we proposed a new morphological classification of HMC, and found that tumor could affect the morphology of HMC in glioma patients. The results can help our clinical practice, enabling us to further understand the spatial structure of the cerebral hemispheres.

Denoising task-correlated head motion from motor-task fMRI data with multi-echo ICA

Motor-task functional magnetic resonance imaging (fMRI) is crucial in the study of several clinical conditions, including stroke and Parkinson's disease. However, motor-task fMRI is complicated by task-correlated head motion, which can be magnified in clinical populations and confounds motor activation results. One method that may mitigate this issue is multi-echo independent component analysis (ME-ICA), which has been shown to separate the effects of head motion from the desired blood oxygenation level dependent (BOLD) signal but has not been tested in motor-task datasets with high amounts of motion. In this study, we collected an fMRI dataset from a healthy population who performed a hand grasp task with and without task-correlated amplified head motion to simulate a motor-impaired population. We analyzed these data using three models: single-echo (SE), multi-echo optimally combined (ME-OC), and ME-ICA. We compared the models' performance in mitigating the effects of head motion on the subject level and group level. On the subject level, ME-ICA better dissociated the effects of head motion from the BOLD signal and reduced noise. Both ME models led to increased t-statistics in brain motor regions. In scans with high levels of motion, ME-ICA additionally mitigated artifacts and increased stability of beta coefficient estimates, compared to SE. On the group level, all three models produced activation clusters in expected motor areas in scans with both low and high motion, indicating that group-level averaging may also sufficiently resolve motion artifacts that vary by subject. These findings demonstrate that ME-ICA is a useful tool for subject-level analysis of motor-task data with high levels of task-correlated head motion. The improvements afforded by ME-ICA are critical to improve reliability of subject-level activation maps for clinical populations in which group-level analysis may not be feasible or appropriate, for example, in a chronic stroke cohort with varying stroke location and degree of tissue damage.

Increased interhemispheric functional connectivity during non-dominant hand movement in right-handed subjects

Hand preference is one of the behavioral expressions of lateralization in the brain. Previous fMRI studies showed the activation in several regions including the motor cortex and the cerebellum during single-hand movement. However, functional connectivity related to hand preference has not been investigated. Here, we used the generalized psychophysiological interaction (gPPI) approach to investigate the alteration of functional connectivity during single-hand movement from the resting state in right-hand subjects. The functional connectivity in interhemispheric motor-related regions including the supplementary motor area, the precentral gyrus, and the cerebellum was significantly increased during non-dominant hand movement, while functional connectivity was not increased during dominant hand movement. The general linear model (GLM) showed activation in contralateral supplementary motor area, contralateral precentral gyrus, and ipsilateral cerebellum during right- or left-hand movement. These results indicate that a combination of GLM and gPPI analysis can detect the lateralization of hand preference more clearly.

Structural Brain Network Reorganization Following Anterior Callosotomy for Colloid Cysts: Connectometry and Graph Analysis Results

Introduction:

The plasticity of the neural circuits after injuries has been extensively investigated over the last decades. Transcallosal microsurgery for lesions affecting the third ventricle offers an interesting opportunity to investigate the whole-brain white matter reorganization occurring after a selective resection of the genu of the corpus callosum (CC).

Method

Diffusion MRI (dMRI) data and neuropsychological testing were collected pre- and postoperatively in six patients with colloid cysts, surgically treated with a transcallosal-transgenual approach. Longitudinal connectometry analysis on dMRI data and graph analysis on structural connectivity matrix were implemented to analyze how white matter pathways and structural network topology reorganize after surgery.

Results

Although a significant worsening in cognitive functions (e.g., executive and memory functioning) at early postoperative, a recovery to the preoperative status was observed at 6 months. Connectometry analysis, beyond the decrease of quantitative anisotropy (QA) near the resection cavity, showed an increase of QA in the body and forceps major CC subregions, as well as in the left intra-hemispheric corticocortical associative fibers. Accordingly, a reorganization of structural network topology was observed between centrality increasing in the left hemisphere nodes together with a rise in connectivity strength among mid and posterior CC subregions and cortical nodes.

Conclusion

A structural reorganization of intra- and inter-hemispheric connective fibers and structural network topology were observed following the resection of the genu of the CC. Beyond the postoperative transient cognitive impairment, it could be argued anterior CC resection does not preclude neural plasticity and may subserve the long-term postoperative cognitive recovery.