Effects of aging on functional connectivity in a neurodegenerative risk cohort: resting state versus task measurement using near-infrared spectroscopy.
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ABSTRACT:
SUBMITTER: Blum L
PROVIDER: S-EPMC9253312 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Near-infrared spectroscopy (NIRS) offers the potential to characterize resting-state functional connectivity (RSFC) in populations that are not easily assessed otherwise, such as young infants. In addition to the advantages of NIRS, one should also consider that the RS-NIRS signal requires specific data preprocessing and analysis. In particular, the RS-NIRS signal shows a colored frequency spectrum, which can be observed as temporal autocorrelation, thereby introducing spurious correlations. To address this issue, prewhitening of the RS-NIRS signal has been recently proposed as a necessary step to remove the signal temporal autocorrelation and therefore reduce false-discovery rates. However, the impact of this step on the analysis of experimental RS-NIRS data has not been thoroughly assessed prior to the present study. Here, the results of a standard preprocessing pipeline in a RS-NIRS dataset acquired in infants are compared with the results after incorporating two different prewhitening algorithms. Our results with a standard preprocessing replicated previous studies. Prewhitening altered RSFC patterns and disrupted the antiphase relationship between oxyhemoglobin and deoxyhemoglobin. We conclude that a better understanding of the effect of prewhitening on RS-NIRS data is still needed before directly considering its incorporation to the standard preprocessing pipeline.
| S-EPMC6200149 | biostudies-literature
Project description:ObjectiveFunctional near-infrared spectroscopy (fNIRS) is a non-invasive and promising tool to map the brain functional networks in stroke recovery. Our study mainly aimed to use fNIRS to detect the different patterns of resting-state functional connectivity (RSFC) in subacute stroke patients with different degrees of upper extremity motor impairment defined by Fugl-Meyer motor assessment of upper extremity (FMA-UE). The second aim was to investigate the association between FMA-UE scores and fNIRS-RSFC among different regions of interest (ROIs) in stroke patients.MethodsForty-nine subacute (2 weeks-6 months) stroke patients with subcortical lesions were enrolled and were classified into three groups based on FMA-UE scores: mild impairment (n = 17), moderate impairment (n = 13), and severe impairment (n = 19). All patients received FMA-UE assessment and 10-min resting-state fNIRS monitoring. The fNIRS signals were recorded over seven ROIs: bilateral dorsolateral prefrontal cortex (DLPFC), middle prefrontal cortex (MPFC), bilateral primary motor cortex (M1), and bilateral primary somatosensory cortex (S1). Functional connectivity (FC) was calculated by correlation coefficients between each channel and each ROI pair. To reveal the comprehensive differences in FC among three groups, we compared FC on the group level and ROI level. In addition, to determine the associations between FMA-UE scores and RSFC among different ROIs, Spearman's correlation analyses were performed with a significance threshold of p < 0.05. For easy comparison, we defined the left hemisphere as the ipsilesional hemisphere and flipped the lesional right hemisphere in MATLAB R2013b.ResultsFor the group-level comparison, the one-way ANOVA and post-hoc t-tests (mild vs. moderate; mild vs. severe; moderate vs. severe) showed that there was a significant difference among three groups (F = 3.42, p = 0.04) and the group-averaged FC in the mild group (0.64 ± 0.14) was significantly higher than that in the severe group (0.53 ± 0.14, p = 0.013). However, there were no significant differences between the mild and moderate group (MD ± SE = 0.05 ± 0.05, p = 0.35) and between the moderate and severe group (MD ± SE = 0.07 ± 0.05, p = 0.16). For the ROI-level comparison, the severe group had significantly lower FC of ipsilesional DLPFC-ipsilesional M1 [p = 0.015, false discovery rate (FDR)-corrected] and ipsilesional DLPFC-contralesional M1 (p = 0.035, FDR-corrected) than those in the mild group. Moreover, the result of Spearman's correlation analyses showed that there were significant correlations between FMA-UE scores and FC of the ipsilesional DLPFC-ipsilesional M1 (r = 0.430, p = 0.002), ipsilesional DLPFC-contralesional M1 (r = 0.388, p = 0.006), ipsilesional DLPFC-MPFC (r = 0.365, p = 0.01), and ipsilesional DLPFC-contralesional DLPFC (r = 0.330, p = 0.021).ConclusionOur findings indicate that different degrees of post-stroke upper extremity impairment reflect different RSFC patterns, mainly in the connection between DLPFC and bilateral M1. The association between FMA-UE scores and the FC of ipsilesional DLPFC-associated ROIs suggests that the ipsilesional DLPFC may play an important role in motor-related plasticity. These findings can help us better understand the neurophysiological mechanisms of upper extremity motor impairment and recovery in subacute stroke patients from different perspectives. Furthermore, it sheds light on the ipsilesional DLPFC-bilateral M1 as a possible neuromodulation target.
| S-EPMC9682186 | biostudies-literature
Project description:Resting-state functional connectivity (rsFC) has gained popularity mainly due to its simplicity and potential for providing insights into various brain disorders. In this vein, functional near-infrared spectroscopy (fNIRS) is an attractive choice due to its portability, flexibility, and low cost, allowing for bedside imaging of brain function. While promising, fNIRS suffers from non-neural signal contaminations (i.e., systemic physiological noise), which can increase correlation across fNIRS channels, leading to spurious rsFC networks. In the present work, we hypothesized that additional measurements with short channels, heart rate, mean arterial pressure, and end-tidal CO2 could provide a better understanding of the effects of systemic physiology on fNIRS-based resting-state networks. To test our hypothesis, we acquired 12 min of resting-state data from 10 healthy participants. Unlike previous studies, we investigated the efficacy of different pre-processing approaches in extracting resting-state networks. Our results are in agreement with previous studies and reinforce the fact that systemic physiology can overestimate rsFC. We expanded on previous work by showing that removal of systemic physiology decreases intra- and inter-subject variability, increasing the ability to detect neural changes in rsFC across groups and over longitudinal studies. Our results show that by removing systemic physiology, fNIRS can reproduce resting-state networks often reported with functional magnetic resonance imaging (fMRI). Finally, the present work details the effects of systemic physiology and outlines how to remove (or at least ameliorate) their contributions to fNIRS signals acquired at rest.
| S-EPMC8957952 | biostudies-literature
Project description:BackgroundThe human brain is a highly complex system that can be represented as a structurally interconnected and functionally synchronized network, which assures both the segregation and integration of information processing. Recent studies have demonstrated that a variety of neuroimaging and neurophysiological techniques such as functional magnetic resonance imaging (MRI), diffusion MRI and electroencephalography/magnetoencephalography can be employed to explore the topological organization of human brain networks. However, little is known about whether functional near infrared spectroscopy (fNIRS), a relatively new optical imaging technology, can be used to map functional connectome of the human brain and reveal meaningful and reproducible topological characteristics.ResultsWe utilized resting-state fNIRS (R-fNIRS) to investigate the topological organization of human brain functional networks in 15 healthy adults. Brain networks were constructed by thresholding the temporal correlation matrices of 46 channels and analyzed using graph-theory approaches. We found that the functional brain network derived from R-fNIRS data had efficient small-world properties, significant hierarchical modular structure and highly connected hubs. These results were highly reproducible both across participants and over time and were consistent with previous findings based on other functional imaging techniques.ConclusionsOur results confirmed the feasibility and validity of using graph-theory approaches in conjunction with optical imaging techniques to explore the topological organization of human brain networks. These results may expand a methodological framework for utilizing fNIRS to study functional network changes that occur in association with development, aging and neurological and psychiatric disorders.
| S-EPMC3454388 | biostudies-literature
Project description:Connectivity impairment has frequently been associated with the pathophysiology of attention-deficit/hyperactivity disorder (ADHD). Although the connectivity of the resting state has mainly been studied, we expect the transition between baseline and task may also be impaired in ADHD children. Twenty-three typically developing (i.e., control) and 36 disordered (ADHD and autism-comorbid ADHD) children were subjected to connectivity analysis. Specifically, they performed an attention task, visual oddball, while their brains were measured by functional near-infrared spectroscopy. The results of the measurements revealed three key findings. First, the control group maintained attentive connectivity, even in the baseline interval. Meanwhile, the disordered group showed enhanced bilateral intra- and interhemispheric connectivities while performing the task. However, right intrahemispheric connectivity was found to be weaker than those for the control group. Second, connectivity and activation characteristics might not be positively correlated with each other. In our previous results, disordered children lacked activation in the right middle frontal gyrus. However, within region connectivity of the right middle frontal gyrus was relatively strong in the baseline interval and significantly increased in the task interval. Third, the connectivity-based biomarker performed better than the activation-based biomarker in terms of screening. Activation and connectivity features were independently optimized and cross validated to obtain the best performing threshold-based classifier. The effectiveness of connectivity features, which brought significantly higher training accuracy than the optimum activation features, was confirmed (88% versus 76%). The optimum screening features were characterized by two trends: (1) strong connectivities of right frontal, left frontal, and left parietal lobes and (2) weak connectivities of left frontal, left parietal, and right parietal lobes in the control group. We conclude that the attentive task-based connectivity effectively shows the difference between control and disordered children and may represent pathological characteristics to be feasibly implemented as a supporting tool for clinical screening.
| S-EPMC6917048 | biostudies-literature
Project description:BackgroundThere was a reorganization of the brain network after stroke. Some studies have compared the characteristics of activation or functional connectivity (FC) of cortical and subcortical regions between the dominant and non-dominant hemisphere stroke.ObjectivesTo analyze hemispheric dominance differences in task-state motor network properties in subacute stroke by functional near-infrared spectroscopy (fNIRS).Materials and methodsPatients with first ischemic stroke in the basal ganglia within 1-3 months after onset and age- and sex-matched right-handed healthy subjects (HS) were enrolled. fNIRS with 29 channels was used to detect the oxyhemoglobin concentration changes when performing the hand grasping task. Activation patterns of motor cortex and two macroscale and two mesoscale brain network indicators based on graph theory were compared between dominant and non-dominant hemisphere stroke.ResultsWe enrolled 17 subjects in each of left hemisphere stroke (LHS), right hemisphere stroke (RHS), and HS groups. Both patient groups showed bilateral activation. The average weighted clustering coefficient and global efficiency of patients were lower than those of healthy people, and the inter-density was higher than that of the HS group, but the significance was different between LHS and RHS groups. The intra-density changes in the RHS group were opposite to those in the LHS group. The correlation between mesoscale indicators and motor function differed between dominant and non-dominant hemisphere stroke.ConclusionThe changes in macroscale cortical network indicators were similar between the two patient groups, while those of the mesoscale indicators were different. The mesoscale brain network characteristics were affected by the severity of dysfunction to varying degrees in the LHS and RHS patients.
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Project description:Studies on functional brain lateralization using functional magnetic resonance imaging (fMRI) have generally focused on lateralization of local brain regions. To explore the lateralization on the whole-brain level, lateralization of functional connectivity using resting-state fMRI (N=87, right handed) was analyzed and left- and right-lateralized networks were mapped. Four hundred two equally spaced regions of interest (ROI) covering the entire gray matter were divided into 358 task-positive and 44 task-negative ROIs. Lateralization of functional connectivity was analyzed separately for the task-positive and task-negative regions to prevent spuriously high lateralization indices caused by negative correlations between task-positive and task-negative regions. Lateralized functional connections were obtained using k-means clustering analysis. Within the task-positive network, the right-lateralized functional connections were between the occipital and inferior/middle frontal regions among other connections, whereas the left-lateralized functional connections were among fusiform gyrus and inferior frontal and inferior/superior parietal regions. Within the task-negative network, the left-lateralized connections were mainly between the precuneus and medial prefrontal regions. Specific brain regions exhibited different left- or right-lateralized connections with other regions, which suggest the importance of reporting lateralized connections over lateralized seed regions. The mean lateralization indices of the left- and right-lateralized connections were correlated, suggesting that the lateralization of connectivity may result from complementary processes between the lateralized networks. The potential functions of the lateralized networks were discussed.
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Project description:Depression is a common psychiatric illness affecting over 300 million people globally. Acupuncture has been reported to be a safe complementary treatment for depression. This study is aimed to investigate the efficacy and mechanism of combining acupuncture with antidepressants in treating depression compared to the sole use of antidepressants. Seventy depression patients were randomly assigned to the treatment group (n = 50) and control group (n = 20). The treatment group received acupuncture combined antidepressants treatment for 3 weeks, while the control group took antidepressants monotherapy for 3 weeks. Among the 70 patients, 40 participants (20 control; 20 treatment) were randomized for studying functional connectivity (FC) of the dorsolateral prefrontal cortex (DLPFC) measured by the functional near-infrared spectroscopy. The primary outcome was HAMD-17 and secondary outcomes were PHQ-9, and the relationships of resting-state FC (rsFC) with the depression severity. PHQ-9 and HAMD-17 scores in the treatment group were significantly lower than those in the control group at Week 3 (p = 0.01) with effect sizes of -0.4 and -0.61 respectively. The rsFC in F1, F3, AF3, AF7, FC3, FC5 (left DLPFC, 10-20 system), AF8, and F6 (right DLPFC) in the treatment group had significant temporal correlation (p < 0.05, FDR corrected) in DLPFC compared to the channels in the control group. No significant correlation was found between the changes of rsFC and depression severity. In conclusion, depressed patients receiving acupuncture combined with antidepressants have improvement of depressive symptoms and the stronger rsFC in the DLPFC compared to those using antidepressants alone.
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Project description:Deviations in activation patterns and functional connectivity have been observed in patients with major depressive disorder (MDD) with prefrontal hemodynamics of patients compared with healthy individuals. The graph-theoretical approach provides useful network metrics for evaluating functional connectivity. The evaluation of functional connectivity during a cognitive task can be used to explain the neurocognitive mechanism underlying the cognitive impairments caused by depression. Overall, 31 patients with MDD and 43 healthy individuals completed a verbal fluency task (VFT) while wearing a head-mounted functional near-infrared spectroscopy (fNIRS) devices. Hemodynamics and functional connectivity across eight prefrontal subregions in the two groups were analyzed and compared. We observed a reduction in prefrontal activation and weaker overall and interhemispheric subregion-wise correlations in the patient group compared with corresponding values in the control group. Moreover, efficiency, the network measure related to the effectiveness of information transfer, showed a significant between-group difference [t (71.64) = 3.66, corrected p < 0.001] along with a strong negative correlation with depression severity (rho = -0.30, p = 0.009). The patterns of prefrontal functional connectivity differed significantly between the patient and control groups during the VFT. Network measures can quantitatively characterize the reduction in functional connectivity caused by depression. The efficiency of the functional network may play an important role in the understanding of depressive symptoms.
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