Fatigue and perceived energy in a sample of older adults over 10 years: A resting state functional connectivity study of neural correlates.

PURPOSE: Declining energy and increasing fatigue, common in older age, predict neurodegenerative conditions, but their neural substrates are not known. We examined brain resting state connectivity in relation to declining self-reported energy levels (SEL) and occurrence of fatigue over time. METHODS: We examined resting-state functional MRI in 272 community dwelling older adults participating in the Health Aging and Body Composition Study (mean age 83 years; 57.4 % female; 40.8 % Black) with measures of fatigue and SEL collected at regular intervals over the prior ten years. Functional connectivity (FC) between cortex and striatum was examined separately for sensorimotor, executive, and limbic functional subregions. Logistic regression tested the association of FC in each network with prior fatigue state (reporting fatigue at least once or never reporting fatigue), and with SEL decline (divided into stable or declining SEL groups) and adjusted for demographic, physical function, mood, cognition, and comorbidities. RESULTS: Higher cortico-striatal FC in the right limbic network was associated with lower odds of reporting fatigue (better) at least once during the study period (adjusted odds ratio [95 % confidence interval], p-value: (0.747 [0.582, 0.955], 0.020), independent of SEL. Higher cortico-striatal FC in the right executive network was associated with higher odds of declining SEL (worse) during the study period (adjusted odds ratio [95 % confidence interval], p-value: (1.31 [1.01, 1.69], 0.041), independent of fatigue. Associations with other networks were not significant. CONCLUSIONS: In this cohort of older adults, the cortico-striatal functional connectivity of declining SEL appears distinct from that underlying fatigue. Studies to further assess the neural correlates of energy and fatigue, and their independent contribution to neurodegenerative conditions are warranted.

Prefrontal cortex activation while walking did not change but gait speed improved after a randomized physical therapy intervention.

BACKGROUND: Higher prefrontal cortex (PFC) activation while walking may indicate reduced gait automaticity. AIM: We examine whether PFC activation during walking improves after training in older adults at risk for mobility disability. METHODS: Forty-two adults aged ≥ 65 participated in a randomized clinical trial (NCT026637780) of a 12-week timing and coordination physical therapy intervention to improve walking (n = 20 intervention, n = 22 active control). PFC activation was measured by functional near-infrared spectroscopy (fNIRS) during four walking tasks over 15 m, each repeated 4 times: even surface walking, uneven surface walking, even dual-task, uneven dual-task; dual-task was reciting every other letter of the alphabet while walking. Gait speed and rate of correct letter generation were recorded. Linear mixed models tested between arm differences in change of fNIRS, gait speed, and letter generation from baseline to follow-up (12-week, 24-week, and 36-week). RESULTS: Intervention arms were similar in mean age (74.3 vs. 77.0) and baseline gait speed (0.96 vs. 0.93 m/s). Of 24 comparisons of between arm differences in the fNIRS signals, only two were significant which were not supported by differences at other follow-up times or on other tasks. Gait speed, particularly during dual-task conditions, and correct letter generation did improve post-intervention but improvements did not differ by arm. DISCUSSION AND CONCLUSIONS: After training, PFC activation during walking generally did not improve and did not differ by intervention arm. Improvements in gait speed without increased PFC activation may point toward more efficient neural control of walking.

Unhealthy white matter connectivity, cognition, and racialization in older adults.

INTRODUCTION: White matter hyperintensities (WMH) may promote clinical Alzheimer's disease (AD) disparities between Black American (BA) and non-Hispanic White (nHW) populations. Using a novel measurement, unhealthy white matter connectivity (UWMC), we interrogated racialized group differences in associations between WMH in AD pathology-affected regions and cognition. METHODS: UWMC is the proportion of white matter fibers that pass through WMH for every pair of brain regions. Individual regression models tested associations of UWMC in beta-amyloid (Aß) or tau pathology-affected regions with cognition overall, stratified by racialized group, and with a racialized group interaction. RESULTS: In 201 older adults ranging from cognitively unimpaired to AD, BA participants exhibited greater UWMC and worse cognition than nHW participants. UWMC was negatively associated with cognition in 17 and 5 Aß- and tau-affected regions, respectively. Racialization did not modify these relationships. DISCUSSION: Differential UWMC burden, not differential UWMC-and-cognition associations, may drive clinical AD disparities between racialized groups. HIGHLIGHTS: Unhealthy white matter connectivity (UWMC) in Alzheimer's disease (AD) pathology-affected brain regions is associated with cognition. Relationships between UWMC and cognition are similar between Black American (BA) and non-Hispanic White (nHW) individuals. More UWMC may partially drive higher clinical AD burden in BA versus nHW populations. UWMC risk factors, particularly social and environmental, should be identified.

Covariate-guided Bayesian mixture of spline experts for the analysis of multivariate high-density longitudinal data.

With rapid development of techniques to measure brain activity and structure, statistical methods for analyzing modern brain-imaging data play an important role in the advancement of science. Imaging data that measure brain function are usually multivariate high-density longitudinal data and are heterogeneous across both imaging sources and subjects, which lead to various statistical and computational challenges. In this article, we propose a group-based method to cluster a collection of multivariate high-density longitudinal data via a Bayesian mixture of smoothing splines. Our method assumes each multivariate high-density longitudinal trajectory is a mixture of multiple components with different mixing weights. Time-independent covariates are assumed to be associated with the mixture components and are incorporated via logistic weights of a mixture-of-experts model. We formulate this approach under a fully Bayesian framework using Gibbs sampling where the number of components is selected based on a deviance information criterion. The proposed method is compared to existing methods via simulation studies and is applied to a study on functional near-infrared spectroscopy, which aims to understand infant emotional reactivity and recovery from stress. The results reveal distinct patterns of brain activity, as well as associations between these patterns and selected covariates.

Targeted multidomain intervention for complex mTBI: protocol for a multisite randomized controlled trial in military-age civilians.

Background: Mild traumatic brain injury (mTBI) affects ~18,000 military personnel each year, and although most will recover in 3-4 weeks, many experience persisting symptoms and impairment lasting months or longer. Current standard of care for U.S. military personnel with complex mTBI involves initial (<48 h) prescribed rest, followed by behavioral (e.g., physical activity, sleep regulation, stress reduction, hydration, nutrition), and symptom-guided management. There is growing agreement that mTBI involves different clinical profiles or subtypes that require a comprehensive multidomain evaluation and adjudication process, as well as a targeted approach to treatment. However, there is a lack of research examining the effectiveness of this approach to assessing and treating mTBI. This multisite randomized controlled trial (RCT) will determine the effectiveness of a targeted multidomain (T-MD) intervention (anxiety/mood, cognitive, migraine, ocular, vestibular; and sleep, autonomic) compared to usual care (behavioral management) in military-aged civilians with complex mTBI. Methods: This study employs a single-blinded, two-group repeated measures design. The RCT will enroll up to 250 military-aged civilians (18-49 yrs) with a diagnosed complex mTBI within 8 days to 6 months of injury from two concussion specialty clinics. The two study arms are a T-MD intervention and a usual care, behavioral management control group. All participants will complete a comprehensive, multidomain clinical evaluation at their first clinical visit. Information gathered from this evaluation will be used to adjudicate mTBI clinical profiles. Participants will then be randomized to either the 4-week T-MD or control arm. The T-MD group will receive targeted interventions that correspond to the patient's clinical profile (s) and the control group will receive behavioral management strategies. Primary outcomes for this study are changes from enrollment to post-intervention on the Neurobehavioral Symptom Inventory (NSI), Patient Global Impression of Change (PGIC), and functional near-infrared spectroscopy (fNIRS). Time to return to activity (RTA), and healthcare utilization costs will also be assessed. Discussion: Study findings may inform a more effective approach to treat complex mTBI in military personnel and civilians, reduce morbidity, and accelerate safe return-to-duty/activity. Ethics and dissemination: The study is approved by the University of Pittsburgh Institutional Review board and registered at clinicaltrials.gov. Dissemination plans include peer-reviewed publications and presentations at professional meetings. Clinical Trial Registration: www.clinicaltrials.gov, identifier: NCT04549532.

Brain space image reconstruction of functional near-infrared spectroscopy using a Bayesian adaptive fused sparse overlapping group lasso model.

Significance: Functional near-infrared spectroscopy (fNIRS) is a noninvasive technology that uses low levels of nonionizing light in the range of red and near-infrared to record changes in the optical absorption and scattering of the underlying tissue that can be used to infer blood flow and oxygen changes during brain activity. The challenges and difficulties of reconstructing spatial images of hemoglobin changes from fNIRS data are mainly caused by the illposed nature of the optical inverse model. Aim: We describe a Bayesian approach combining several lasso-based regularizations to apply anatomy-prior information to solving the inverse model. Approach: We built a Bayesian hierarchical model to solve the Bayesian adaptive fused sparse overlapping group lasso (Ba-FSOGL) model. The method is evaluated and validated using simulation and experimental datasets. Results: We apply this approach to the simulation and experimental datasets to reconstruct a known brain activity. The reconstructed images and statistical plots are shown. Conclusion: We discuss the adaptation of this method to fNIRS data and demonstrate that this approach provides accurate image reconstruction with a low false-positive rate, through numerical simulations and application to experimental data collected during motor and sensory tasks.

Mother-child neural synchronization is time linked to mother-child positive affective state matching.

In the first years of life, in which self-regulation occurs via external means, mother-child synchronization of positive affect (PA) facilitates regulation of child homeostatic systems. Mother-child affective synchrony may contribute to mother-child synchronization of neural systems, but limited research has explored this possibility. Participants were 41 healthy mother-child dyads (56% girls; Mage = 24.76 months; s.d. = 8.77 months, Range = 10-42 months). Mothers' and children's brain activities were assessed simultaneously using near-infrared spectroscopy while engaging in dyadic play. Mother and child PA during play were coded separately to characterize periods in which mothers and children (i) matched on high PA, (ii) matched on low/no PA or (iii) showed a mismatch in PA. Models evaluated moment-to-moment correlations between affective matching and neural synchrony in mother-child dyads. Greater positive affective synchrony, in which mother and child showed similarly high levels of PA but not similarly low levels of PA, was related to greater synchrony in medial and lateral frontal and temporoparietal regions. Age moderated associations between mother and child neural activities but only during moments of high PA state matching. Positive, synchronous mother-child interactions may foster greater neural responding in affective and social regions important for self-regulation and interpersonal bonds.

Introduction to the shared near infrared spectroscopy format.

Significance: Functional near-infrared spectroscopy (fNIRS) is a popular neuroimaging technique with proliferating hardware platforms, analysis approaches, and software tools. There has not been a standardized file format for storing fNIRS data, which has hindered the sharing of data as well as the adoption and development of software tools. Aim: We endeavored to design a file format to facilitate the analysis and sharing of fNIRS data that is flexible enough to meet the community's needs and sufficiently defined to be implemented consistently across various hardware and software platforms. Approach: The shared NIRS format (SNIRF) specification was developed in consultation with the academic and commercial fNIRS community and the Society for functional Near Infrared Spectroscopy. Results: The SNIRF specification defines a format for fNIRS data acquired using continuous wave, frequency domain, time domain, and diffuse correlation spectroscopy devices. Conclusions: We present the SNIRF along with validation software and example datasets. Support for reading and writing SNIRF data has been implemented by major hardware and software platforms, and the format has found widespread use in the fNIRS community.

The use of functional near infrared spectroscopy and gait analysis to characterize cognitive and motor processing in early-stage patients with multiple sclerosis.

Background: Dual-task paradigms are a known tool to evaluate possible impairments in the motor and cognitive function in patients with multiple sclerosis (MS). A technique to evaluate the cortical function during movement is functional near-infrared spectroscopy (fNIRS). The evaluation of the MS course or its treatment by associating fNIRS with gait measurements may be flexible and low-cost; however, there are no feasibility studies in the literature using these combined techniques in early-stage patients with MS. Objective: To evaluate cortical hemodynamics using fNIRS and gait parameters in patients at early stages of MS and in healthy controls during a dual-task paradigm. Methods: Participants performed cognitive tasks while walking to simulate daily activities. Cortical activation maps and gait variability were used to evaluate differences between 19 healthy controls and 20 patients with MS. Results and conclusion: The results suggest an enhanced cortical activation in the motor planning areas already at the early stages of MS when compared to controls. We have also shown that a systematic analysis of the spatiotemporal gait variability parameters indicates differences in the patient population. The association of cortical and gait parameters may reveal possible compensatory mechanisms related to gait during dual tasking at the early stages of the disease.

Correction of global physiology in resting-state functional near-infrared spectroscopy.

Significance: Resting-state functional connectivity (RSFC) analyses of functional near-infrared spectroscopy (fNIRS) data reveal cortical connections and networks across the brain. Motion artifacts and systemic physiology in evoked fNIRS signals present unique analytical challenges, and methods that control for systemic physiological noise have been explored. Whether these same methods require modification when applied to resting-state fNIRS (RS-fNIRS) data remains unclear. Aim: We systematically examined the sensitivity and specificity of several RSFC analysis pipelines to identify the best methods for correcting global systemic physiological signals in RS-fNIRS data. Approach: Using numerically simulated RS-fNIRS data, we compared the rates of true and false positives for several connectivity analysis pipelines. Their performance was scored using receiver operating characteristic analysis. Pipelines included partial correlation and multivariate Granger causality, with and without short-separation measurements, and a modified multivariate causality model that included a non-traditional zeroth-lag cross term. We also examined the effects of pre-whitening and robust statistical estimators on performance. Results: Consistent with previous work on bivariate correlation models, our results demonstrate that robust statistics and pre-whitening are effective methods to correct for motion artifacts and autocorrelation in the fNIRS time series. Moreover, we found that pre-filtering using principal components extracted from short-separation fNIRS channels as part of a partial correlation model was most effective in reducing spurious correlations due to shared systemic physiology when the two signals of interest fluctuated synchronously. However, when there was a temporal lag between the signals, a multivariate Granger causality test incorporating the short-separation channels was better. Since it is unknown if such a lag exists in experimental data, we propose a modified version of Granger causality that includes the non-traditional zeroth-lag term as a compromising solution. Conclusions: A combination of pre-whitening, robust statistical methods, and partial correlation in the processing pipeline to reduce autocorrelation, motion artifacts, and global physiology are suggested for obtaining statistically valid connectivity metrics with RS-fNIRS. Further studies should validate the effectiveness of these methods using human data.

Scale invariance in fNIRS as a measurement of cognitive load.

Scale invariant neural dynamics are a relatively new but effective means of measuring changes in brain states as a result of varied cognitive load and task difficulty. This study tests whether scale invariance (as measured by the Hurst exponent, H) can be used with functional near-infrared spectroscopy (fNIRS) to quantify cognitive load, paving the way for scale-invariance to be measured in a variety of real-world settings. We analyzed H extracted from the fNIRS time series while participants completed an N-back working memory task. Consistent with what has been demonstrated in fMRI, the current results showed that scale-invariance analysis significantly differentiated between task and rest periods as calculated from both oxy- (HbO) and deoxy-hemoglobin (HbR) concentration changes. Results from both channel-averaged H and a multivariate partial least squares approach (Task PLS) demonstrated higher H during the 1-back task than the 2-back task. These results were stronger for H derived from HbR than from HbO. This suggests that scale-free brain states are a robust signature of cognitive load and not limited by the specific neuroimaging modality employed. Further, as fNIRS is relatively portable and robust to motion-related artifacts, these preliminary results shed light on the promising future of measuring cognitive load in real life settings.

Influence of serial subtraction tasks on transient characteristics of postural control.

We sought to better understand the influence of cognitive perturbations on transient aspects of postural control. Twenty healthy, younger adults had their postural control assessed during eyes open quiet stance. Participants completed three different conditions that either had no cognitive perturbation present, an easy cognitive perturbation (i.e., serial subtraction by ones), or a more difficult cognitive perturbation (i.e., serial subtraction by sevens). All trials finished with 60 s of undisturbed eyes open quiet stance, which was the focus of the balance assessment. 95% confidence ellipse area (EA) was calculated for 5-s epochs throughout the trial. The difference in EA from the first epoch after participants started (onset) or stopped (offset) the cognitive task to the last epoch of the trial (i.e., 55-60 s after perturbation) was used to characterize transient postural control behavior. Functional near-infrared spectroscopy was also used to quantify changes in prefrontal cortex activation during the counting tasks to support interpretation of the transient balance findings. There was a significant effect of condition for transient balance characteristics following a cognitive perturbation (P < 0.001), with greater transient increases in postural sway for both difficult (Cohen's d = 0.40, P < 0.001) and easier (Cohen's d = 0.29, P = 0.013) cognitive perturbations relative to no cognitive perturbation. The onset of cognitive tasks was also associated with greater transient increases in postural sway than the offset of the cognitive tasks (Cohen's d = 0.24, P = 0.019). The functional near-infrared spectroscopy data indicated that a significant decrease in deoxygenated hemoglobin was observed for left Brodmann area 46 for both the subtraction by ones (T = -3.97; Benjamini-Hochberg significance value (q) = 0.008) and subtraction by sevens (T = -3.11; q = 0.036) conditions relative to the baseline condition. The subtraction by sevens condition was also associated with a relative increase in deoxygenated hemoglobin for the right Brodmann area 9 (T = 3.36; q = 0.026) compared to the subtraction by ones condition. In conclusion, serial subtraction can elicit transient increases in postural sway, with more difficult tasks and the onset of the cognitive-motor challenge exhibiting magnified effects. Additionally, even the cessation of a cognitive task (i.e., serial subtraction) can be associated with lingering perturbing effects on balance control.

Offset analgesia is associated with opposing modulation of medial versus dorsolateral prefrontal cortex activations: A functional near-infrared spectroscopy study.

Offset analgesia is defined by a dramatic drop in perceived pain intensity with a relatively small decrease in noxious input. Although functional magnetic resonance imaging studies implicate subcortical descending inhibitory circuits during offset analgesia, the role of cortical areas remains unclear. The current study identifies cortical correlates of offset analgesia using functional near infrared spectroscopy (fNIRS). Twenty-four healthy volunteers underwent fNIRS scanning during offset (OS) and control (Con) heat stimuli applied to the forearm. After controlling for non-neural hemodynamic responses in superficial tissues, widespread increases in cortical oxygenated hemoglobin concentration were observed, reflecting cortical activation during heat pain. OS-Con contrasts revealed deactivations in bilateral medial prefrontal cortex (mPFC) and bilateral somatosensory cortex (SSC) associated with offset analgesia. Right dorsolateral prefrontal cortex (dlPFC) showed activation only during OS. These data demonstrate opposing cortical activation patterns during offset analgesia and support a model in which right dlPFC underlies ongoing evaluation of pain intensity change. With predictions of decreasing pain intensity, right dlPFC activation likely inhibits ascending noxious input via subcortical pathways resulting in SSC and mPFC deactivation. This study identifies cortical circuitry underlying offset analgesia and introduces the use of fNIRS to study pain modulation in an outpatient clinical environment.

Cortico-striatal functional connectivity and cerebral small vessel disease: Contribution to mild Parkinsonian signs.

BACKGROUND AND PURPOSE: Mild Parkinsonian signs (MPS) are common in older adults. We hypothesized that MPS are associated with lower functional connectivity (FC) in dopamine-dependent cortico-striatal networks, and these associations vary with white matter hyperintensity (WMH), a risk factor for MPS. METHODS: We examined resting-state functional MRI in 266 participants (mean age 83; 57% female; 41% African American) with data on MPS (Unified Parkinson's Disease Rating Scale), demographics, cognition, muscle-skeletal, and cardiometabolic health. FC between cortex and striatum was examined separately for sensorimotor, executive, and limbic functional subregions. Logistic regression tested the association of FC in each network with MPS, adjusted for covariates. Interactions of FC by WMH were tested; and analyses were repeated stratified by WMH above/below the median. RESULTS: Compared to those without MPS, those with MPS had lower cortico-striatal FC in the left executive network (adjusted odds ratio [95% confidence interval], p-value: 0.188 [0.043, 0.824], .027). The interaction with WMH was p = .064; left executive FC was inversely associated with MPS for high WMH (0.077 [0.010, 0.599], .014) but not low WMH participants (1.245 [0.128, 12.132], .850). CONCLUSIONS: MPS appear related to lower executive network FC, robust to adjustment for other risk factors, and stronger for those with higher burden of WMH. Future longitudinal studies should examine the interplay between cerebral small vessel disease and connectivity influencing MPS.

Postpartum Depression Is Associated With Altered Neural Connectivity Between Affective and Mentalizing Regions During Mother-Infant Interactions.

Although there has been growing interest in mood-related neural alterations in women in the initial weeks postpartum, recent work has demonstrated that postpartum depression often lingers for months or years following birth. However, research evaluating the impact of depression on maternal brain function during mother-infant interactions in the late postpartum period is lacking. The current study tested the hypothesis that depressive symptoms at 12-months postpartum are associated with neural alterations in affective and social neural regions, using near-infrared spectroscopy during in vivo mother-infant interactions. Participants were 23 birth mothers of 12-month-old infants (60% boys). While undergoing near-infrared spectroscopy, mothers engaged in an ecologically valid interactive task in which they looked at an age-appropriate book with their infants. Mothers also reported on their depressive symptoms in the past week and were rated on their observed levels of maternal sensitivity during mother-infant play. Greater depressive severity at 12-months postpartum was related to lower connectivity between the right temporoparietal junction and the lateral prefrontal cortex, but greater connectivity between the right temporoparietal junction and anterior medial prefrontal cortex during mother-infant interaction. Given the putative functions of these neural regions within the maternal brain network, our findings suggest that in the context of depression, postpartum mothers' mentalizing about her infants' thoughts and feelings may be related to lower ability to express and regulate her own emotions, but greater ability to engage in emotional bonding with her infant. Future work should explore how connectivity among these regions is associated with longitudinal changes in maternal behavior, especially in the context of changes in mothers' depressive symptoms (e.g., with treatment) over time.

Dysfunction in interpersonal neural synchronization as a mechanism for social impairment in autism spectrum disorder.

Social deficits in autism spectrum disorder (ASD) have been linked to atypical activation of the mentalizing network. This work, however, has been limited by a focus on the brain activity of a single person during computerized social tasks rather than exploring brain activity during in vivo interactions. The current study assessed neural synchronization during a conversation as a mechanism for social impairment in adults with ASD (n = 24) and matched controls (n = 26). Functional near-infrared spectroscopy (fNIRS) data were collected from the prefrontal cortex (PFC) and tempoparietal junction (TPJ). Participants self-reported on their social communication and videos of the interaction were coded for utterances and conversational turns. As expected, controls showed more neural synchrony than participants with ASD in the TPJ. Also as expected, controls showed less social communication impairment than participants with ASD. However, participants with ASD did not have fewer utterances compared with control subjects. Overall, less neural synchrony in the TPJ was associated with higher social impairment and marginally fewer utterances. Our findings advance our understanding of social difficulties in ASD by linking them to decreased neural synchronization of the TPJ. LAY SUMMARY: The coordination of brain responses is important for efficient social interactions. The current study explored the coordination of brain responses in neurotypical adults and adults with ASD to investigate if difficulties in social interactions are related to difficulties coordinating brain responses in ASD. We found that participants with ASD had more difficulties coordinating brain responses during a conversation with an interacting partner. Additionally, we found that the level of coordination in brain responses was linked to problems with social communication.

Cardiac-induced cerebral pulsatility, brain structure, and cognition in middle and older-aged adults.

Changes of cardiac-induced regional pulsatility can be associated with specific regions of brain volumetric changes, and these are related with cognitive alterations. Thus, mapping of cardiac pulsatility over the entire brain can be helpful to assess these relationships. A total of 108 subjects (age: 66.5 ± 8.4 years, 68 females, 52 healthy controls, 11 subjective cognitive decline, 17 impaired without complaints, 19 MCI and 9 AD) participated. The pulsatility map was obtained directly from resting-state functional MRI time-series data at 3T. Regional brain volumes were segmented from anatomical MRI. Multidomain neuropsychological battery was performed to test memory, language, attention and visuospatial construction. The Montreal Cognitive Assessment (MoCA) was also administered. The sparse partial least square (SPLS) method, which is desirable for better interpreting high-dimensional variables, was applied for the relationship between the entire brain voxels of pulsatility and 45 segmented brain volumes. A multiple holdout SPLS framework was used to optimize sparsity for assessing the pulsatility-volume relationship model and to test the reliability by fitting the models to 9 different splits of the data. We found statistically significant associations between subsets of pulsatility voxels and subsets of segmented brain volumes by rejecting the omnibus null hypothesis (any of 9 splits has p < 0.0056 (=0.05/9) with the Bonferroni correction). The pulsatility was positively associated with the lateral ventricle, choroid plexus, inferior lateral ventricle, and 3rd ventricle and negatively associated with hippocampus, ventral DC, and thalamus volumes for the first pulsatility-volume relationship. The pulsatility had an additional negative relationship with the amygdala and brain stem volumes for the second pulsatility-volume relationship. The spatial distribution of correlated pulsatility was observed in major feeding arteries to the brain regions, ventricles, and sagittal sinus. The indirect mediating pathways through the volumetric changes were statistically significant between the pulsatility and multiple cognitive measures (p < 0.01). Thus, the cerebral pulsatility, along with volumetric measurements, could be a potential marker for better understanding of pathophysiology and monitoring disease progression in age-related neurodegenerative disorders.

How do neural processes give rise to cognition? Simultaneously predicting brain and behavior with a dynamic model of visual working memory.

There is consensus that activation within distributed functional brain networks underlies human thought. The impact of this consensus is limited, however, by a gap that exists between data-driven correlational analyses that specify where functional brain activity is localized using functional magnetic resonance imaging (fMRI), and neural process accounts that specify how neural activity unfolds through time to give rise to behavior. Here, we show how an integrative cognitive neuroscience approach may bridge this gap. In an exemplary study of visual working memory, we use multilevel Bayesian statistics to demonstrate that a neural dynamic model simultaneously explains behavioral data and predicts localized patterns of brain activity, outperforming standard analytic approaches to fMRI. The model explains performance on both correct trials and incorrect trials where errors in change detection emerge from neural fluctuations amplified by neural interaction. Critically, predictions of the model run counter to cognitive theories of the origin of errors in change detection. Results reveal neural patterns predicted by the model within regions of the dorsal attention network that have been the focus of much debate. The model-based analysis suggests that key areas in the dorsal attention network such as the intraparietal sulcus play a central role in change detection rather than working memory maintenance, counter to previous interpretations of fMRI studies. More generally, the integrative cognitive neuroscience approach used here establishes a framework for directly testing theories of cognitive and brain function using the combined power of behavioral and fMRI data. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Enhanced spatiotemporal resolution imaging of neuronal activity using joint electroencephalography and diffuse optical tomography.

Significance: Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) are both commonly used methodologies for neuronal source reconstruction. While EEG has high temporal resolution (millisecond-scale), its spatial resolution is on the order of centimeters. On the other hand, in comparison to EEG, fNIRS, or diffuse optical tomography (DOT), when used for source reconstruction, can achieve relatively high spatial resolution (millimeter-scale), but its temporal resolution is poor because the hemodynamics that it measures evolve on the order of several seconds. This has important neuroscientific implications: e.g., if two spatially close neuronal sources are activated sequentially with only a small temporal separation, single-modal measurements using either EEG or DOT alone would fail to resolve them correctly. Aim: We attempt to address this issue by performing joint EEG and DOT neuronal source reconstruction. Approach: We propose an algorithm that utilizes DOT reconstruction as the spatial prior of EEG reconstruction, and demonstrate the improvements using simulations based on the ICBM152 brain atlas. Results: We show that neuronal sources can be reconstructed with higher spatiotemporal resolution using our algorithm than using either modality individually. Further, we study how the performance of the proposed algorithm can be affected by the locations of the neuronal sources, and how the performance can be enhanced by improving the placement of EEG electrodes and DOT optodes. Conclusions: We demonstrate using simulations that two sources separated by 2.3-3.3 cm and 50 ms can be recovered accurately using the proposed algorithm by suitably combining EEG and DOT, but not by either in isolation. We also show that the performance can be enhanced by optimizing the electrode and optode placement according to the locations of the neuronal sources.

Load-dependent relationships between frontal fNIRS activity and performance: A data-driven PLS approach.

Neuroimaging research frequently demonstrates load-dependent activation in prefrontal and parietal cortex during working memory tasks such as the N-back. Most of this work has been conducted in fMRI, but functional near-infrared spectroscopy (fNIRS) is gaining traction as a less invasive and more flexible alternative to measuring cortical hemodynamics. Few fNIRS studies, however, have examined how working memory load-dependent changes in brain hemodynamics relate to performance. The current study employs a newly developed and robust statistical analysis of task-based fNIRS data in a large sample, and demonstrates the utility of data-driven, multivariate analyses to link brain activation and behavior in this modality. Seventy participants completed a standard N-back task with three N-back levels (N = 1, 2, 3) while fNIRS data were collected from frontal and parietal cortex. Overall, participants showed reliably greater fronto-parietal activation for the 2-back versus the 1-back task, suggesting fronto-parietal fNIRS measurements are sensitive to differences in cognitive load. The results for 3-back were much less consistent, potentially due to poor behavioral performance in the 3-back task. To address this, a multivariate analysis (behavioral partial least squares, PLS) was conducted to examine the interaction between fNIRS activation and performance at each N-back level. Results of the PLS analysis demonstrated differences in the relationship between accuracy and change in the deoxyhemoglobin fNIRS signal as a function of N-back level in eight mid-frontal channels. Specifically, greater reductions in deoxyhemoglobin (i.e., more activation) were positively related to performance on the 3-back task, unrelated to accuracy in the 2-back task, and negatively associated with accuracy in the 1-back task. This pattern of results suggests that the metabolic demands correlated with neural activity required for high levels of accuracy vary as a consequence of task difficulty/cognitive load, whereby more automaticity during the 1-back task (less mid-frontal activity) predicted superior performance on this relatively easy task, and successful engagement of this mid-frontal region was required for high accuracy on a more difficult and cognitively demanding 3-back task. In summary, we show that fNIRS activity can track working memory load and can uncover significant associations between brain activity and performance, thus opening the door for this modality to be used in more wide-spread applications.