Resting-state Functional Near-infrared Spectroscopy Research Articles

Association of Exercise with Better Olfactory Performance and Higher Functional Connectivity Between the Olfactory Cortex and the Prefrontal Cortex: A Resting-State Functional Near-Infrared Spectroscopy Study.

Background: Olfactory deterioration is suggested to be a predictor of some neurodegenerative diseases. Recent studies indicate that physical exercise has a positive relationship with olfactory performance, and a subregion in the prefrontal cortex (PFC) may play an important role in olfactory processing. The PFC is not only related to olfactory function but it also engages in complex functions such as cognition and emotional processing. Methodology: Our study compared the functional connectivity between the olfactory cortex and the PFC in healthy individuals who exercised regularly and healthy persons who did not. Those who exercised more than three times/week for at least 30 min each time were considered the exercise group, and those who did not meet this exercise criteria were considered the nonexercise group. We also assessed their odor threshold. Participants were aged 55 years or older, and the two groups were balanced for age, sex, body mass index, and educational level. Results: We found that compared with individuals who did not exercise, exercisers had a significantly lower threshold for detecting odors. In addition, the olfactory cortex had stronger connectivity with the PFC in exercisers than in nonexercisers. More specifically, when the PFC was grouped into three subregions, namely, the ventrolateral prefrontal cortex (VLPFC), dorsolateral prefrontal cortex (DLPFC), and frontopolar cortex (FPA), Pearson correlation analysis revealed stronger connectivity between the VLPFC and the orbitofrontal cortex (OFC), between the OFC and the FPA, and between the left and right OFC hemispheres in the exercisers. In addition, Granger causality indicated higher directional connectivity from the DLPFC to the OFC in exercisers than in nonexercisers. Conclusion: Our findings indicated that the exercise group not only had better olfactory performance but also had stronger functional connectivity between the olfactory cortex and the PFC than nonexercise group.

Resting-State Functional Connectivity Predicts Cochlear-Implant Speech Outcomes.

Cochlear implants (CIs) have revolutionized hearing restoration for individuals with severe or profound hearing loss. However, a substantial and unexplained variability persists in CI outcomes, even when considering subject-specific factors such as age and the duration of deafness. In a pioneering study, we use resting-state functional near-infrared spectroscopy to predict speech-understanding outcomes before and after CI implantation. Our hypothesis centers on resting-state functional connectivity (FC) reflecting brain plasticity post-hearing loss and implantation, specifically targeting the average clustering coefficient in resting FC networks to capture variation among CI users. Twenty-three CI candidates participated in this study. Resting-state functional near-infrared spectroscopy data were collected preimplantation and at 1 month, 3 months, and 1 year postimplantation. Speech understanding performance was assessed using consonant-nucleus-consonant words in quiet and Bamford-Kowal-Bench sentences in noise 1-year postimplantation. Resting-state FC networks were constructed using regularized partial correlation, and the average clustering coefficient was measured in the signed weighted networks as a predictive measure for implantation outcomes. Our findings demonstrate a significant correlation between the average clustering coefficient in resting-state functional networks and speech understanding outcomes, both pre- and postimplantation. This approach uses an easily deployable resting-state functional brain imaging metric to predict speech-understanding outcomes in implant recipients. The results indicate that the average clustering coefficient, both pre- and postimplantation, correlates with speech understanding outcomes.

Convolutional neural networks can detect orthostatic hypotension in Parkinson's disease using resting-state functional near-infrared spectroscopy data.

Neurological disorders such as Parkinson's disease (PD) often adversely affect the vascular system, leading to alterations in blood flow patterns. Functional near-infrared spectroscopy (fNIRS) is used to monitor hemodynamic changes via signal measurement. This study investigated the potential of using resting-state fNIRS data through a convolutional neural network (CNN) to evaluate PD with orthostatic hypotension. The CNN demonstrated significant efficacy in analyzing fNIRS data, and it outperformed the other machine learning methods. The results indicate that judicious input data selection can enhance accuracy by over 85%, while including the correlation matrix as an input further improves the accuracy to more than 90%. This study underscores the promising role of CNN-based fNIRS data analysis in the diagnosis and management of the PD. This approach enhances diagnostic accuracy, particularly in resting-state conditions, and can reduce the discomfort and risks associated with current diagnostic methods, such as the head-up tilt test.

Functional connectivity alterations in the frontoparietal network and sensorimotor network are associated with behavioral heterogeneity in blepharospasm.

Primary blepharospasm (BSP) is a clinically heterogeneous disease that manifests not only as spasmodic closure of the eyelids but also sometimes with apraxia of eyelid opening (AEO). This cross-sectional study aimed to investigate differences in the neural mechanisms of isolated BSP and BSP-associated AEO subtypes, which may reveal the pathophysiology underlying different phenotypes. A total of 29 patients manifested as isolated BSP, 17 patients manifested as BSP associated with AEO, and 28 healthy controls underwent resting-state functional near-infrared spectroscopy (fNIRS). We assessed functional connectivity (FC) between regions of interest (ROIs) in the fronto-parietal control network (PFCN) and sensorimotor network (SMN). We also examined the relationship between altered FC and behavioral data. In the FPCN, ROI- analyses showed decreased FC between the left premotor cortex and supramarginal gyrus in the BSP with AEO group compared to the isolated BSP group. In the SMN, both subgroups showed hypoconnectivity of the left premotor cortex with the right primary motor cortex, primary sensory cortex, and somatosensory association cortex. This hypoconnectivity was positively correlated with the total number of botulinum toxin A treatments, which suggests that long-term botulinum toxin A treatment may modulate motor sequence planning and coordination. These findings showed different connectivity alterations in neural networks associated with motor and cognitive control among different behavioral phenotypes of BSP. The identification of specific alterations in various networks that correspond to clinical heterogeneity may inform the identification of potential biomarkers for early diagnosis and personalized neuromodulation targets for treating different BSP subphenotypes.

Relationship between 4-month functional brain network topology and 24-month neurodevelopmental outcome in children with congenital heart disease

Survivors of complex forms of congenital heart disease (CHD)∗ are at high risk of neurodevelopmental disabilities. Neuroimaging studies have pointed to brain anomalies and immature networks in infants with CHD, yet less is known about their functional network topology and associations with neurodevelopment. To characterize the functional network topology in 4-month-old infants with repaired CHD, we compared graph theory metrics measured using resting-state functional near-infrared spectroscopy (rs-fNIRS) between infants with CHD (n = 22) and healthy controls (n = 30). We also investigated the moderating effect of graph theory metrics on the relationship between group (CHD vs. Controls) and developmental outcomes at 24 months. At 4 months, both groups presented similar functional brain network topology. At 24 months, children with CHD had lower scores on the language scale and the expressive communication subscale of the Bayley Scales of Infant and Toddler Development, Third Edition (Bayley-III), as well as lower scores on the Grammatical Form scale of the MacArthur-Bates Communicative Development Inventory (MBCDI). The relationship between group and expressive language was moderated by the normalized characteristic path length (λ) and the degree (k). Although infants with CHD have functional brain topology similar to that of healthy controls, our findings suggest that they do not benefit from an optimal functional brain organization in comparison with healthy infants.

Intrinsic brain activity is increasingly complex and develops asymmetrically during childhood and early adolescence

A large body of evidence suggests that brain signal complexity (BSC) may be an important indicator of healthy brain functioning or alternately, a harbinger of disease and dysfunction. However, despite recent progress our current understanding of how BSC emerges and evolves in large-scale networks, and the factors that shape these dynamics, remains limited. Here, we utilized resting-state functional near-infrared spectroscopy (rs-fNIRS) to capture and characterize the nature and time course of BSC dynamics within large-scale functional networks in 107 healthy participants ranging from 6-13 years of age. Age-dependent increases in spontaneous BSC were observed predominantly in higher-order association areas including the default mode (DMN) and attentional (ATN) networks. Our results also revealed asymmetrical developmental patterns in BSC that were specific to the dorsal and ventral ATN networks, with the former showing a left-lateralized and the latter demonstrating a right-lateralized increase in BSC. These age-dependent laterality shifts appeared to be more pronounced in females compared to males. Lastly, using a machine-learning model, we showed that BSC is a reliable predictor of chronological age. Higher-order association networks such as the DMN and dorsal ATN demonstrated the most robust prognostic power for predicting ages of previously unseen individuals. Taken together, our findings offer new insights into the spatiotemporal patterns of BSC dynamics in large-scale intrinsic networks that evolve over the course of childhood and adolescence, suggesting that a network-based measure of BSC represents a promising approach for tracking normative brain development and may potentially aid in the early detection of atypical developmental trajectories.

Functional connectivity changes in infants with varying degrees of unilateral hearing loss.

It has been proved that unilateral hearing loss (UHL) can cause functional connectivity alterations in adults. However, the mechanism of the human brain coping with the challenge of unilateral hearing deprivation at very early developmental phases remains poorly understood. Here, we performed a resting-state functional near-infrared spectroscopy (fNIRS) study on 3- to 10-month-old infants with varying degrees of unilateral hearing loss to investigate the effect of unilateral auditory deprivation in infants. Using network-based statistics, increased functional connectivity was observed in single-sided deafness (SSD) compared with normal hearing infants, and the right middle temporal gyrus was the most involved nodes. In addition, changes in cortical function in infants were related to the degree of hearing loss, with significantly increased functional connectivity in infants with severe to profound unilateral hearing loss compared with the ones with mild to moderate. Moreover, more significant cortical functional recombination changes were found in right-SSD than in left-SSD infants. For the first time, our study provides evidence for the effects of unilateral hearing deprivation on the early cortical development of the human brain, which would also act as a reference for intervention decisions in children with unilateral hearing loss in clinical settings.

Neuroplasticity Following Stroke from a Functional Laterality Perspective: A fNIRS Study.

To explore alterations of resting-state functional connectivity (rsFC) in sensorimotor cortex following strokes with left or right hemiplegia considering the lateralization and neuroplasticity. Seventy-three resting-state functional near-infrared spectroscopy (fNIRS) files were selected, including 26 from left hemiplegia (LH), 21 from right hemiplegia (RH) and 26 from normal controls (NC) group. Whole-brain analyses matching the Pearson correlation were used for rsFC calculations. For right-handed normal controls, rsFC of motor components (M1 and M2) in the left hemisphere displayed a prominent intensity in comparison with the right hemisphere (p < 0.05), while for stroke groups, this asymmetry has disappeared. Additionally, RH rather than LH showed stronger rsFC between left S1 and left M1 in contrast to normal controls (p < 0.05), which correlated inversely with motor function (r = -0.53, p < 0.05). Regarding M1, rsFC within ipsi-lesioned M1 has a negative correlation with motor function of the affected limb (r = -0.60 for the RH group and -0.43 for the LH group, p < 0.05). The rsFC within contra-lesioned M1 that innervates the normal side was weakened compared with that of normal controls (p < 0.05). Stronger rsFC of motor components in left hemisphere was confirmed by rs-fNIRS as the "secret of dominance" for the first time, while post-stroke hemiplegia broke this cortical asymmetry. Meanwhile, a statistically strengthened rsFC between left S1 and M1 only in right-hemiplegia group may act as a compensation for the impairment of the dominant side. This research has implications for brain-computer interfaces synchronizing sensory feedback with motor performance and transcranial magnetic regulation for cortical excitability to induce cortical plasticity.

Transcranial direct current stimulation over the right dorsolateral prefrontal cortex increases oxyhemoglobin concentration and cognitive performance dependent on cognitive load

Transcranial direct current stimulation (tDCS) has been explored as a potential method for cognitive enhancement. tDCS may induce a cascade of neurophysiological changes including alterations in cerebral oxygenation. However, the effects of tDCS on the cognitive-cerebral oxygenation interaction remains unclear. Further, oxygenation variability across individuals remains minimally controlled for. The purpose of this sham-controlled study was to test the effects of anodal tDCS over the right dorsolateral prefrontal cortex (DLPFC) on the interaction between working memory and cerebral oxygenation while controlling for individual oxygenation variability. Thirty-three adults received resting-state functional near-infrared spectroscopy (fNIRS) recordings over bilateral prefrontal cortices. Following this, working memory was tested using a Toulouse n-back task concurrently paired with fNIRS, with measurements taken before and after 20 min of anodal or sham tDCS at 1.5 mA. With individual oxygenation controlled for, anodal tDCS was found to increase the oxyhemoglobin concentration over the right DLPFC during the 2-back (q = .015) and 3-back (q = .008) conditions. Additionally, anodal tDCS was found to improve accuracy during the 3-back task by 13.4 % (p = .028) and decrease latency by 250 ms (p = .013). The increase in oxyhemoglobin was strongly correlated with increases in accuracy (p = .041) and decreases in latency during the 3-back span (p = .017). Taken together, anodal tDCS over the right DLPFC was found to regionally increase oxyhemoglobin concentrations and improve working memory performance in higher cognitive load conditions.

Disturbed functional connectivity and topological properties of the frontal lobe in minimally conscious state based on resting-state fNIRS.

Patients in minimally conscious state (MCS) exist measurable evidence of consciousness. The frontal lobe is a crucial part of the brain that encodes abstract information and is closely related to the conscious state. We hypothesized that the disturbance of the frontal functional network exists in MCS patients. We collected the resting-state functional near-infrared spectroscopy (fNIRS) data of fifteen MCS patients and sixteen age- and gender-matched healthy controls (HC). The Coma Recovery Scale-Revised (CRS-R) scale of MCS patients was also composed. The topology of the frontal functional network was analyzed in two groups. Compared with HC, the MCS patients showed widely disrupted functional connectivity in the frontal lobe, especially in the frontopolar area and right dorsolateral prefrontal cortex. Moreover, the MCS patients displayed lower clustering coefficient, global efficiency, local efficiency, and higher characteristic path length. In addition, the nodal clustering coefficient and nodal local efficiency in the left frontopolar area and right dorsolateral prefrontal cortex were significantly reduced in MCS patients. Furthermore, the nodal clustering coefficient and nodal local efficiency in the right dorsolateral prefrontal cortex were positively correlated to auditory subscale scores. This study reveals that MCS patients' frontal functional network is synergistically dysfunctional. And the balance between information separation and integration in the frontal lobe is broken, especially the local information transmission in the prefrontal cortex. These findings help us to understand the pathological mechanism of MCS patients better.

The relationship between the prefrontal cortex and limb motor function in stroke: A study based on resting-state functional near-infrared spectroscopy

BackgroundWith the ageing of the world population, the incidence of stroke has been increasing annually, becoming a public health problem affecting adult health. Limb motor dysfunction is one of the common complications of stroke and an important factor in disability. Therefore, restoring limb function is an important task in current rehabilitation. Accurate assessment of motor function in stroke patients is the basis for formulating effective rehabilitation strategies. With the development of neuroimaging technology, scholars have begun to study objective evaluation methods for limb motor dysfunction in stroke to determine reliable neural biomarkers to accurately identify brain functional activity and its relationship with limb motor function. The prefrontal cortex (PFC) plays an important role in motor control and in response to motor state changes. Our previous study found that the PFC network characteristics of stroke patients are related to their motor function status and the topological properties of the PFC network under resting state can predict the motor function of stroke patients to some extent. Therefore, this study used functional near-infrared spectroscopy (fNIRS) to evaluate prefrontal neuroplasticity markers and the relationships between such neural markers and limb motor function in stroke patients with limb motor dysfunction, which could be helpful to further clarify the relationship between brain neuroplasticity and cerebral haemodynamics. At the same time, through accurate and objective means of evaluation, it could be helpful for clinicians to formulate and optimize individualized rehabilitation treatment plans and accurately determine the rehabilitation efficacy and prognosis. MethodsThis study recruited 17 S patients with limb motor dysfunction and 9 healthy subjects. fNIRS was used to collect 22 channels of cerebral blood oxygen signals in the PFC in the resting state. The differences in prefrontal oxygenated haemoglobin (HbO) and deoxygenated haemoglobin (HbR) concentrations were analysed between stroke patients and healthy subjects, and the lateralization index (LI) of HbO in stroke patients was also calculated. Pearson’s correlation analysis was performed between the LI and the scores of the Fugl-Meyer Assessment Scale (FMA) of motor function in stroke patients. ResultsThe results found that the prefrontal HbO concentration was significantly decreased in stroke patients with limb motor dysfunction compared with healthy subjects, and there was a significant, positive correlation between the LI of the PFC and FMA scores in stroke patients. ConclusionThese study results showed that stroke can cause cerebral haemodynamic changes in the PFC, and the functional imbalance of the left and right PFC in the resting state is correlated with the severity of limb motor dysfunction. Furthermore, we emphasize that the cerebral haemodynamic activity reflected by fNIRS could be used as a reliable neural biomarker for assessing limb motor dysfunction in stroke.

Functional brain connectivity after corrective cardiac surgery for critical congenital heart disease: a preliminary near-infrared spectroscopy (NIRS) report

ABSTRACT Patients with congenital heart disease (CHD) requiring cardiac surgery in infancy are at high risk for neurodevelopmental impairments. Neonatal imaging studies have reported disruptions of brain functional organization before surgery. Yet, the extent to which functional network alterations are present after cardiac repair remains unexplored. This preliminary study aimed at investigating cortical functional connectivity in 4-month-old infants with repaired CHD, using resting-state functional near-infrared spectroscopy (fNIRS). After fNIRS signal frequency decomposition, we compared values of magnitude-squared coherence as a measure of connectivity strength, between 21 infants with corrected CHD and 31 healthy controls. We identified a subset of connections with differences between groups at an uncorrected statistical level of p < .05 while controlling for sex and maternal socioeconomic status, with most of these connections showing reduced connectivity in infants with CHD. Although none of these differences reach statistical significance after FDR correction, likely due to the small sample size, moderate to large effect sizes were found for group-differences. If replicated, these results would therefore suggest preliminary evidence that alterations of brain functional connectivity are present in the months after cardiac surgery. Additional studies involving larger sample size are needed to replicate our data, and comparisons between pre- and postoperative findings would allow to further delineate alterations of functional brain connectivity in this population.

Effectiveness of cerebellar vermis intermittent theta-burst stimulation in improving trunk control and balance function for patients with subacute stroke: a randomised controlled trial protocol

IntroductionBalance impairments frequently occur after stroke. Achieving effective core trunk stability is the key to improving balance ability. However, there is still a lack of advanced well-defined rehabilitation protocols for...

Screening for Alzheimer's disease using prefrontal resting-state functional near-infrared spectroscopy.

Alzheimer's disease (AD) is neurodegenerative dementia that causes neurovascular dysfunction and cognitive impairment. Currently, 50 million people live with dementia worldwide, and there are nearly 10 million new cases every year. There is a need for relatively less costly and more objective methods of screening and early diagnosis. Functional near-infrared spectroscopy (fNIRS) systems are a promising solution for the early Detection of AD. For a practical clinically relevant system, a smaller number of optimally placed channels are clearly preferable. In this study, we investigated the number and locations of the best-performing fNIRS channels measuring prefrontal cortex activations. Twenty-one subjects diagnosed with AD and eighteen healthy controls were recruited for the study. We have shown that resting-state fNIRS recordings from a small number of prefrontal locations provide a promising methodology for detecting AD and monitoring its progression. A high-density continuous-wave fNIRS system was first used to verify the relatively lower hemodynamic activity in the prefrontal cortical areas observed in patients with AD. By using the episode averaged standard deviation of the oxyhemoglobin concentration changes as features that were fed into a Support Vector Machine; we then showed that the accuracy of subsets of optical channels in predicting the presence and severity of AD was significantly above chance. The results suggest that AD can be detected with a 0.76 sensitivity score and a 0.68 specificity score while the severity of AD could be detected with a 0.75 sensitivity score and a 0.72 specificity score with ≤5 channels. These scores suggest that fNIRS is a viable technology for conveniently detecting and monitoring AD as well as investigating underlying mechanisms of disease progression.

Studying hemispheric lateralization of 4-month-old infants from different language groups through near-infrared spectroscopy-based connectivity.

Early monolingual versus bilingual experience affects linguistic and cognitive processes during the first months of life, as well as functional activation patterns. The previous study explored the influence of a bilingual environment in the first months of life on resting-state functional connectivity and reported no significant difference between language groups. To further explore the influence of a bilingual environment on brain development function, we used the resting-state functional near-infrared spectroscopy public dataset of the 4-month-old infant group in the sleep state (30 Spanish; 33 Basque; 36 bilingual). Wavelet Transform Coherence, graph theory, and Granger causality methods were performed on the functional connectivity of the frontal lobes. The results showed that functional connectivity strength was significantly higher in the left hemisphere than that in the right hemisphere in both monolingual and bilingual groups. The graph theoretic analysis showed that the characteristic path length was significantly higher in the left hemisphere than in the right hemisphere for the bilingual infant group. Contrary to the monolingual infant group, the left-to-right direction of information flow was found in the frontal regions of the bilingual infant group in the effective connectivity analysis. The results suggested that the left hemispheric lateralization of functional connectivity in frontal regions is more pronounced in the bilingual group compared to the monolingual group. Furthermore, effective connectivity analysis may be a useful method to investigate the resting-state brain networks of infants.

Intrinsic Organization of Occipital Hubs Predicts Depression: A Resting-State fNIRS Study.

Dysfunctional brain networks have been found in patients with major depressive disorder (MDD). In this study, to verify this in a more straightforward way, we investigated the intrinsic organization of brain networks in MDD by leveraging the resting-state functional near-infrared spectroscopy (rs-fNIRS). Thirty-four MDD patients (24 females, 38.41 ± 13.14 years old) and thirty healthy controls (22 females, 34.43 ± 5.03 years old) underwent a 10 min rest while their brain activity was recorded via fNIRS. The results showed that MDD patients and healthy controls exhibited similar resting-state functional connectivity. Moreover, the depression group showed lower small-world Lambda (1.12 ± 0.04 vs. 1.16 ± 0.10, p = 0.04) but higher global efficiency (0.51 ± 0.03 vs. 0.48 ± 0.05, p = 0.03) than the control group. Importantly, MDD patients, as opposed to healthy controls, showed a significantly lower nodal local efficiency at the left middle occipital gyrus (0.56 ± 0.36 vs. 0.81 ± 0.20, pFDR &lt; 0.05), which predicted the level of depression in MDD (r = 0.45, p = 0.01, R2 = 0.15). In sum, we found a more integrated brain network in MDD patients with a lower nodal local efficiency at the occipital hub, which could predict depressive symptoms.

Identifying autism spectrum disorder in resting-state fNIRS signals based on multiscale entropy and a two-branch deep learning network

BackgroundThe demand for early and precise identification of autism spectrum disorder (ASD) presented a challenge to the prediction of ASD with a non-invasive neuroimaging method. New methodA deep learning model was proposed to identify children with ASD using the resting-state functional near-infrared spectroscopy (fNIRS) signals. In this model, the input was the pattern of brain complexity represented by multiscale entropy of fNIRS time-series signals, with the purpose to solve the problem of deep learning analysis when the raw signals were limited by length and the number of subjects. The model consisted of a two-branch deep learning network, where one branch was a convolution neural network and the other was a long short-term memory neural network based on an attention mechanism. ResultsOur model could achieve an identification accuracy of 94%. Further analysis used the SHapley Additive exPlanations (SHAP) method to balance the accuracy and the number of optical channels, thus reducing the complexity of fNIRS experiment. Comparison with previously used method(s)in identification accuracy, our model was about 14% higher than previously used deep learning models with the same input and 4% higher than the same model but directly using fNIRS signals as input. We could obtain a discriminative accuracy of 90% with nearly half of the measurement channels by the SHAP method. ConclusionsUsing the pattern of brain complexity as input was effective in the deep learning model when the fNIRS signals were insufficient. With the SHAP method, it was possible to reduce the number of optical channels, while maintaining high accuracy in ASD identification.

Atypical brain network development of infants at elevated likelihood for autism spectrum disorder during the first year of life.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by behavioral features that appear early in life. Although studies have shown that atypical brain functional and structural connectivity are associated with these behavioral traits, the occurrence and initial alterations of brain networks have not been fully investigated. The current study aimed to map early brain network efficiency and information transferring in infants at elevated likelihood (EL) compared to infants at typical likelihood (TL) for ASD in the first year of life. This study used a resting-state functional near-infrared spectroscopy (fNIRS) approach to obtain the length and strength of functional connections in the frontal and temporal areas in 45 5-month-old and 38 10-month-old infants. Modular organization and small-world properties were detected in both EL and TL infants at 5 and 10months. In 5-month-old EL infants, local and nodal efficiency were significantly greater than age-matched TL infants, indicating overgrown local connections. Furthermore, we used a support vector machine (SVM) model to classify infants with or without EL based on the obtained global properties of the network, achieving an accuracy of 77.6%. These results suggest that infants with EL for ASD exhibit inefficiencies in the organization of brain networks during the first year of life.

Effects of Long-Term Exposure to 2260 m Altitude on Working Memory and Resting-State Activity in the Prefrontal Cortex: A Large-Sample Cross-Sectional Study.

It has been well established that very-high-altitude (>4000 m) environments can affect human cognitive function and brain activity. However, the effects of long-term exposure to moderate altitudes (2000–3000 m) on cognitive function and brain activity are not well understood. In the present cross-sectional study, we utilized an N-back working memory task and resting-state functional near-infrared spectroscopy to examine the effects of two years of exposure to 2260 m altitude on working memory and resting-state brain activity in 208 college students, compared with a control group at the sea level. The results showed that there was no significant change in spatial working memory performance after two years of exposure to 2260 m altitude. In contrast, the analysis of resting-state brain activity revealed changes in functional connectivity patterns in the prefrontal cortex (PFC), with the global efficiency increased and the local efficiency decreased after two years of exposure to 2260 m altitude. These results suggest that long-term exposure to moderate altitudes has no observable effect on spatial working memory performance, while significant changes in functional connectivity and brain network properties could possibly occur to compensate for the effects of mild hypoxic environments. To our knowledge, this study is the first to examine the resting state activity in the PFC associated with working memory in people exposed to moderate altitudes.

Associations between maternal prenatal depression and neonatal behavior and brain function – Evidence from the functional near-infrared spectroscopy

BackgroundMaternal prenatal depression is a significant public health issue associated with mental disorders of offspring. This study aimed to determine if maternal prenatal depressive symptoms are associated with changes in neonatal behaviors and brain function at the resting state. MethodsA total of 204 pregnant women were recruited during the third trimester and were evaluated by Edinburgh Postpartum Depression Scale (EPDS). The mother-infant pairs were divided into the depressed group (n = 75) and control group (n = 129) based on the EPDS, using a cut-off value of 10. Cortisol levels in the cord blood and maternal blood collected on admission for delivery were measured. On day three of life, all study newborns were evaluated by the Neonatal Behavior Assessment Scale (NBAS) and 165 infants were evaluated by resting-state functional near-infrared spectroscopy (rs-fNIRS). To minimize the influences of potential bias on the rs-fNIRS results, we used a binary logistic regression model to carry out propensity score matching between the depressed group and the control group. Rs-fNIRS data from 21 pairs of propensity score-matched newborns were used for analysis. The associations between maternal EPDS scores, neonatal NBAS scores, and cortisol levels were analyzed using linear regressions and the mediation analysis models. ResultsCompared to the control group, the newborns in the depressed group had lower scores in the social-interaction and autonomic system dimensions of NBAS (P < 0.01). Maternal and umbilical cord plasma cortisol levels in the depressed group were higher (P < 0.01) than in the control group. However, only umbilical cord plasma cortisol played a negative mediating role in the relationship between maternal EPDS and NBAS in the social-interaction and autonomic system (β med = −0.054 [−0.115,−0.018] and −0.052 [−0.105,−0.019]. Proportional mediation was 13.57 % and 12.33 for social-interaction and autonomic systems, respectively. The newborns in the depressed group showed decreases in the strength of rs-fNIRS functional connections, primarily the connectivity of the left frontal-parietal and temporal-parietal regions. However, infants in the depressed and control groups showed no differences in topological characteristics of the brain network, including standardized clustering coefficient, characteristic path length, small-world property, global efficiency, and local efficiency (P > 0.05). The social-interaction Z-scores had positive correlations with functional connectivity strength of left prefrontal cortex-left parietal lobe (r = 0.57, p < 0.01)，prefrontal cortex-left parietal lobe - left temporal lobe (r = 0.593, p < 0.01) and left parietal lobe - left temporal lobe (r = 0.498, p < 0.01). Autonomic system Z-scores were also significantly positive correlation with prefrontal cortex-left parietal lobe (r = 0.509, p < 0.01)，prefrontal cortex-left parietal lobe - left temporal lobe (r = 0.464, p < 0.01), left parietal lobe - left temporal lobe (r = 0.381, p < 0.05), and right temporal lobe and left temporal lobe (r = 0.310, p < 0.05). ConclusionThis study shows that maternal prenatal depression may affect the development of neonatal social-interaction and autonomic system and the strength of neonatal brain functional connectivity. The fetal cortisol may play a role in behavioral development in infants exposed to maternal prenatal depression. Our findings highlight the importance of prenatal screening for maternal depression and early postnatal behavioral evaluation that provide the opportunity for early diagnosis and intervention to improve neurodevelopmental outcomes.