Increased prefrontal cortex connectivity during cognitive challenge assessed by fNIRS imaging

In this study, functional near-infrared spectroscopy (fNIRS) and the graph theory approach were used to access the functional connectivity (FC) of the prefrontal cortex (PFC) in a resting state and during increased mental workload. For this very purpose, a pattern recognition-based test was developed, which elicited a strong response throughout the PFC during the test condition. FC parameters obtained during stimulation were found increased compared to those in a resting state after correlation based signal improvement (CBSI), which can attenuate those components of fNIRS signals which are unrelated to neural activity. These results indicate that the cognitive challenge increased the FC in the PFC and suggests a great potential in investigating FC in various cognitive states. © 2017 Optical Society of America

Regional optimum frequency analysis of resting-state fMRI data for early detection of Alzheimer’s disease biomarkers

The blood-oxygen label dependent (BOLD) signal obtained from functional magnetic resonance images (fMRI) varies significantly among populations. Yet, there is some agreement among researchers over the pace of the blood flow within several brain regions relative to the subject’s age and cognitive ability. Our analysis further suggested that regional coherence among the BOLD fMRI voxels belonging to the individual region of the brain has some correlation with underlying pathology as well as cognitive performance, which can suggest potential biomarkers to the early onset of the disease. To capitalise on this we propose a method, called Regional Optimum Frequency Analysis (ROFA), which is based on finding the optimum synchrony frequency observed at each brain region for each of the resting-state BOLD frequency bands (Slow 5 (0.01–0.027 Hz), Slow 4 (0.027–0.073 Hz) and slow 3 (0.073 to 0.198 Hz)), and the whole frequency band (0.01–0.167 Hz) respectively. The ROFA is carried out on fMRI data of total 310 scans, i.e., 26, 175 and 109 scans from 21 young-healthy (YH), 69 elderly-healthy (EH) and 33 Alzheimer’s disease (AD) patients respectively, where these scans include repeated scans from some subjects acquired at 3 to 6 months intervals. A 10-fold cross-validation procedure evaluated the performance of ROFA for classification between the YH vs EH, YH vs AD and EH vs AD subjects. Based on the confusion-matrix parameters; accuracy, precision, sensitivity and Matthew’s correlation coefficient (MCC), the proposed ROFA classification outperformed the state-of-the-art Group-independent component analysis (Group-ICA), Functional-connectivity, Graph metrics, Eigen-vector centrality, Amplitude of low-frequency fluctuation (ALFF) and fractional amplitude of low-frequency fluctuations (fALFF) based methods with more than 94.99% precision and 95.67% sensitivity for different subject groups. The results demonstrate the effectiveness of the proposed ROFA parameters (frequencies) as adequate biomarkers of Alzheimer’s disease

Regional optimum frequency analysis of resting-state fMRI data for early detection of Alzheimer’s disease biomarkers

The blood-oxygen label dependent (BOLD) signal obtained from functional magnetic resonance images (fMRI) varies significantly among populations. Yet, there is some agreement among researchers over the pace of the blood flow within several brain regions relative to the subject’s age and cognitive ability. Our analysis further suggested that regional coherence among the BOLD fMRI voxels belonging to the individual region of the brain has some correlation with underlying pathology as well as cognitive performance, which can suggest potential biomarkers to the early onset of the disease. To capitalise on this we propose a method, called Regional Optimum Frequency Analysis (ROFA), which is based on finding the optimum synchrony frequency observed at each brain region for each of the resting-state BOLD frequency bands (Slow 5 (0.01–0.027 Hz), Slow 4 (0.027–0.073 Hz) and slow 3 (0.073 to 0.198 Hz)), and the whole frequency band (0.01–0.167 Hz) respectively. The ROFA is carried out on fMRI data of total 310 scans, i.e., 26, 175 and 109 scans from 21 young-healthy (YH), 69 elderly-healthy (EH) and 33 Alzheimer’s disease (AD) patients respectively, where these scans include repeated scans from some subjects acquired at 3 to 6 months intervals. A 10-fold cross-validation procedure evaluated the performance of ROFA for classification between the YH vs EH, YH vs AD and EH vs AD subjects. Based on the confusion-matrix parameters; accuracy, precision, sensitivity and Matthew’s correlation coefficient (MCC), the proposed ROFA classification outperformed the state-of-the-art Group-independent component analysis (Group-ICA), Functional-connectivity, Graph metrics, Eigen-vector centrality, Amplitude of low-frequency fluctuation (ALFF) and fractional amplitude of low-frequency fluctuations (fALFF) based methods with more than 94.99% precision and 95.67% sensitivity for different subject groups. The results demonstrate the effectiveness of the proposed ROFA parameters (frequencies) as adequate biomarkers of Alzheimer’s disease