Identifying Resting-State Functional Connectivity Changes in the Motor Cortex Using fNIRS During Recovery from Stroke.

Resting-state functional imaging has been used to study the functional reorganization of the brain. The application of functional near-infrared spectroscopy (fNIRS) to assess resting-state functional connectivity (rsFC) has already been demonstrated in recent years. The present study aimed to identify the difference in rsFC patterns during the recovery from the upper-limb deficit due to stroke. Twenty patients with mild stroke having an onset of four to eight weeks were recruited from the stroke clinic of our institute and an equal number of healthy volunteers were included in the study after ethical committee approval. The fNIRS signals were recorded bilaterally over the premotor area and supplementary motor area and over the primary motor cortex. Pearson Correlation is the method used to compute rsFC for the healthy group and patient group. For the healthy group, both intra-hemispheric and inter-hemispheric connections were stronger. RSFC analysis demonstrated changes from the healthy pattern for the patient group with an upper-limb deficit. The left hemisphere affected group showed disrupted ipsilesional and an increased contra-lesional connectivity. The longitudinal data analysis of rsFC showed improvement in the connections in the ipsilesional hemisphere between the primary motor area, somatosensory area, and premotor areas. In the future, the rsFC changes during the recovery could be used to predict the extent of recovery from stroke motor deficits.

Resting fMRI as an alternative for task-based fMRI for language lateralization in temporal lobe epilepsy patients: a study using independent component analysis.

PURPOSE: Our aim is to investigate whether rs-fMRI can be used as an effective technique to study language lateralization. We aim to find out the most appropriate language network among different networks identified using ICA. METHODS: Fifteen healthy right-handed subjects, sixteen left, and sixteen right temporal lobe epilepsy patients prospectively underwent MR scanning in 3T MRI (GE Discovery™ MR750w), using optimized imaging protocol. We obtained task-fMRI data using a visual-verb generation paradigm. Rs-fMRI and language-fMRI analysis were conducted using FSL software. Independent component analysis (ICA) was used to estimate rs-fMRI networks. Dice coefficient was calculated to examine the similarity in activated voxels of a common language template and the rs-fMRI language networks. Laterality index (LI) was calculated from the task-based language activation and rs-fMRI language network, for a range of LI thresholds at different z scores. RESULTS: Measurement of hemispheric language dominance with rs-fMRI was highly concordant with task-fMRI results. Among the evaluated z scores for a range of LI thresholds, rs-fMRI yielded a maximum accuracy of 95%, a sensitivity of 83%, and specificity of 92.8% for z = 2 at 0.05 LI threshold. CONCLUSION: The present study suggests that rs-fMRI networks obtained using ICA technique can be used as an alternative for task-fMRI language laterality. The novel aspect of the work is suggestive of optimal thresholds while applying rs-fMRI, is an important endeavor given that many patients with epilepsy have co-morbid cognitive deficits. Thus, an accurate method to determine language laterality without requiring a patient to complete the language task would be advantageous.

Multiband fMRI as a plausible, time-saving technique for resting-state data acquisition: Study on functional connectivity mapping using graph theoretical measures.

BACKGROUND AND OBJECTIVES: Ensuring patient comfort and compliance by emphasizing reduced time frame for image acquisition, without compromising image quality is the key aspect with functional MRI examination. Multiband resting state fMRI (MB-rsfMRI) is a fairly new technique that potentially shortens MR image acquisition time by providing increased number of time points. The study aims to compare signal characteristics as well as the functional connectivity using conventional resting-state fMRI (rsfMRI) with that of MB-rsfMRI technique. MATERIALS AND METHODS: 9 healthy volunteers have prospectively undergone conventional resting-state fMRI and Multiband rsfMRI scanning technique in a 3T GE scanner (Discovery MR750w™). We compared the temporal SNR (tSNR) of conventional rs-fMRI with that of MB-rsfMRI. We looked at the language network connectivity and small world network characteristics from graph theoretical measures to compare the two techniques. RESULTS: We computed the tSNR of conventional resting-state fMRI (rsfMRI) and MB-rsfMRI technique. A strong positive correlation was seen between graph theoretical measures from MB-rsfMRI and conventional rsfMRI (Pearson Correlation, r = 0.99). Both techniques showed similar small world network characteristics in healthy controls. CONCLUSION: The present study demonstrates negligible differences between the conventional-rsfMRI and MB-rsfMRI acquisitions on the computed graph theoretic measures. Accordingly current analysis proves that MB-rs-fMRI may be used as a time reducing acquisition technique that enables mapping of functional connectivity with similar outcome as conventional rs-fMRI in healthy subjects.

Functional near-infrared spectroscopy is in moderate accordance with functional MRI in determining lateralisation of frontal language areas.

Purpose Understanding language dominance is crucial in pre-surgical evaluation of patients with epilepsy and in patients having a tumour close to the language area. Functional magnetic resonance imaging (fMRI) studies are well established in evaluating language dominance. Functional near-infrared spectroscopy (fNIRS) is a non-invasive optical imaging modality that offers a convenient and affordable technique to image language-related cortical areas. This study investigates the agreement between results from task-based fMRI and fNIRS in determining language lateralisation. Methods Language laterality indices LIs were calculated from both fMRI and fNIRS measurements of the same individual volunteers by using an identical paradigm. Statistical measures of percentage agreement and kappa value have been calculated for testing agreement and reliability. Results A correlation analysis of the LI values shows a good correlation with r = 0.677 at p < 0.05. Statistical comparison of both fMRI and fNIRS methods for language lateralisation yielded a percentage agreement of 90% and a moderate kappa value of κ = 0.621. Conclusion Our study suggests that fNIRS is in moderate accordance with fMRI in determining lateralisation of the frontal language areas. It implies that the optical imaging technique can provide additional information on functional lateralisation of frontal language areas.

Resting-State Seed-Based Analysis: An Alternative to Task-Based Language fMRI and Its Laterality Index.

BACKGROUND AND PURPOSE: Language is a cardinal function that makes human unique. Preservation of language function poses a great challenge for surgeons during resection. The aim of the study was to assess the efficacy of resting-state fMRI in the lateralization of language function in healthy subjects to permit its further testing in patients who are unable to perform task-based fMRI. MATERIALS AND METHODS: Eighteen healthy right-handed volunteers were prospectively evaluated with resting-state fMRI and task-based fMRI to assess language networks. The laterality indices of Broca and Wernicke areas were calculated by using task-based fMRI via a voxel-value approach. We adopted seed-based resting-state fMRI connectivity analysis together with parameters such as amplitude of low-frequency fluctuation and fractional amplitude of low-frequency fluctuation (fALFF). Resting-state fMRI connectivity maps for language networks were obtained from Broca and Wernicke areas in both hemispheres. We performed correlation analysis between the laterality index and the z scores of functional connectivity, amplitude of low-frequency fluctuation, and fALFF. RESULTS: Pearson correlation analysis between signals obtained from the z score of fALFF and the laterality index yielded a correlation coefficient of 0.849 (P < .05). Regression analysis of the fALFF with the laterality index yielded an R2 value of 0.721, indicating that 72.1% of the variance in the laterality index of task-based fMRI could be predicted from the fALFF of resting-state fMRI. CONCLUSIONS: The present study demonstrates that fALFF can be used as an alternative to task-based fMRI for assessing language laterality. There was a strong positive correlation between the fALFF of the Broca area of resting-state fMRI with the laterality index of task-based fMRI. Furthermore, we demonstrated the efficacy of fALFF for predicting the laterality of task-based fMRI.

Resting state fMRI: A review on methods in resting state connectivity analysis and resting state networks.

The inquisitiveness about what happens in the brain has been there since the beginning of humankind. Functional magnetic resonance imaging is a prominent tool which helps in the non-invasive examination, localisation as well as lateralisation of brain functions such as language, memory, etc. In recent years, there is an apparent shift in the focus of neuroscience research to studies dealing with a brain at 'resting state'. Here the spotlight is on the intrinsic activity within the brain, in the absence of any sensory or cognitive stimulus. The analyses of functional brain connectivity in the state of rest have revealed different resting state networks, which depict specific functions and varied spatial topology. However, different statistical methods have been introduced to study resting state functional magnetic resonance imaging connectivity, yet producing consistent results. In this article, we introduce the concept of resting state functional magnetic resonance imaging in detail, then discuss three most widely used methods for analysis, describe a few of the resting state networks featuring the brain regions, associated cognitive functions and clinical applications of resting state functional magnetic resonance imaging. This review aims to highlight the utility and importance of studying resting state functional magnetic resonance imaging connectivity, underlining its complementary nature to the task-based functional magnetic resonance imaging.

Fractal analysis: fractal dimension and lacunarity from MR images for differentiating the grades of glioma.

Glioma, the heterogeneous tumors originating from glial cells, generally exhibit varied grades and are difficult to differentiate using conventional MR imaging techniques. When this differentiation is crucial in the disease prognosis and treatment, even the advanced MR imaging techniques fail to provide a higher discriminative power for the differentiation of malignant tumor from benign ones. A powerful image processing technique applied to the imaging techniques is expected to provide a better differentiation. The present study focuses on the fractal analysis of fluid attenuation inversion recovery MR images, for the differentiation of glioma. For this, we have considered the most important parameters of fractal analysis, fractal dimension and lacunarity. While fractal analysis assesses the malignancy and complexity of a fractal object, lacunarity gives an indication on the empty space and the degree of inhomogeneity in the fractal objects. Box counting method with the preprocessing steps namely binarization, dilation and outlining was used to obtain the fractal dimension and lacunarity in glioma. Statistical analysis such as one-way analysis of variance and receiver operating characteristic (ROC) curve analysis helped to compare the mean and to find discriminative sensitivity of the results. It was found that the lacunarity of low and high grade gliomas vary significantly. ROC curve analysis between low and high grade glioma for fractal dimension and lacunarity yielded 70.3% sensitivity and 66.7% specificity and 70.3% sensitivity and 88.9% specificity, respectively. The study observes that fractal dimension and lacunarity increases with an increase in the grade of glioma and lacunarity is helpful in identifying most malignant grades.

Relative percentage signal intensity recovery of perfusion metrics­an efficient tool for differentiating grades of glioma.

OBJECTIVE: Glioma classification and characterization may be facilitated by a multiparametric approach of perfusion metrics, which could not be achieved by conventional MRI alone. Our aim is to explore the potential of relative percentage signal intensity recovery (rPSR) values, in addition to relative cerebral blood volume (rCBV) and relative cerebral blood flow (rCBF) of first-pass T2* dynamic susceptibility contrast (DSC) perfusion MRI, in differentiating high- and low-grade glioma. METHODS: This prospective study included 39 patients with low-grade and 25 patients with high-grade glioma. rPSR, rCBV and rCBF were calculated from the first-pass T2* DSC perfusion MRI. rPSR was calculated using standard software and validated with dedicated perfusion metrics analysis software. The statistical analysis was performed using analysis of variance and receiver operating characteristic (ROC) curves. RESULTS: Variation in rPSR, rCBV and rCBF values between low- and high-grade gliomas were statistically significant (p < 0.005). The ROC curve analysis for each of them yielded 96% sensitivity and 71.8% specificity; 88% sensitivity and 69.2% specificity; and 72% sensitivity and 66.7% specificity. The area under the curve (AUC) from the ROC curve analysis yielded 0.893, 0.852 and 0.702 for rPSR, rCBV and rCBF, respectively. The rPSR calculation with the validation software yielded 92.3% sensitivity and 72% specificity with an AUC of 0.864. CONCLUSION: rPSR inversely correlates while rCBV and rCBF values directly correlate with the tumour grade. Furthermore, the overall diagnostic performance of rPSR is better than rCBV and rCBF values. ADVANCES IN KNOWLEDGE: rPSR of T2* DSC perfusion is an indicator of blood-brain barrier status and lesion leakiness, which has not been explored yet compared with the usual haemodynamic parameters, rCBV and rCBF.