Feasibility of regional center telehealth visits utilizing a rural research network in people with Parkinson's disease.

Background: Impaired motor and cognitive function can make travel cumbersome for People with Parkinson's disease (PwPD). Over 50% of PwPD cared for at the University of Arkansas for Medical Sciences (UAMS) Movement Disorders Clinic reside over 30 miles from Little Rock. Improving access to clinical care for PwPD is needed. Objective: To explore the feasibility of remote clinic-to-clinic telehealth research visits for evaluation of multi-modal function in PwPD. Methods: PwPD residing within 30 miles of a UAMS Regional health center were enrolled and clinic-to-clinic telehealth visits were performed. Motor and non-motor disease assessments were administered and quantified. Results were compared to participants who performed at-home telehealth visits using the same protocols during the height of the COVID pandemic. Results: Compared to the at-home telehealth visit group (n = 50), the participants from regional centers (n = 13) had similar age and disease duration, but greater disease severity with higher total Unified Parkinson's disease rating scale scores (Z = -2.218, p = 0.027) and lower Montreal Cognitive Assessment scores (Z = -3.350, p < 0.001). Regional center participants had lower incomes (Pearson's chi = 21.3, p < 0.001), higher costs to attend visits (Pearson's chi = 16.1, p = 0.003), and lived in more socioeconomically disadvantaged neighborhoods (Z = -3.120, p = 0.002). Prior research participation was lower in the regional center group (Pearson's chi = 4.5, p = 0.034) but both groups indicated interest in future research participation. Conclusions: Regional center research visits in PwPD in medically underserved areas are feasible and could help improve access to care and research participation in these traditionally underrepresented populations.

Impact of feeding habits on the development of language-specific processing of phonemes in brain: An event-related potentials study.

Introduction: Infancy is a stage characterized by multiple brain and cognitive changes. In a short time, infants must consolidate a new brain network and develop two important properties for speech comprehension: phonemic normalization and categorical perception. Recent studies have described diet as an essential factor in normal language development, reporting that breastfed infants show an earlier brain maturity and thus a faster cognitive development. Few studies have described a long-term effect of diet on phonological perception. Methods: To explore that effect, we compared the event-related potentials (ERPs) collected during an oddball paradigm (frequent /pa/80%, deviant/ba/20%) of infants fed with breast milk (BF), cow-milk-based formula (MF), and soy-based formula (SF), which were assessed at 3, 6, 9, 12, and 24 months of age [Mean across all age groups: 127 BF infants, Mean (M) 39.6 gestation weeks; 121 MF infants, M = 39.16 gestation weeks; 116 SF infants, M = 39.16 gestation weeks]. Results: Behavioral differences between dietary groups in acoustic comprehension were observed at 24-months of age. The BF group displayed greater scores than the MF and SF groups. In phonological discrimination task, the ERPs analyses showed that SF group had an electrophysiological pattern associated with difficulties in phonological-stimulus awareness [mismatch negativity (MMN)-2 latency in frontal left regions of interest (ROI) and longer MMN-2 latency in temporal right ROI] and less brain maturity than BF and MF groups. The SF group displayed more right-lateralized brain recruitment in phonological processing at 12-months old. Discussion: We conclude that using soy-based formula in a prolonged and frequent manner might trigger a language development different from that observed in the BF or MF groups. The soy-based formula's composition might affect frontal left-brain area development, which is a nodal brain region in phonological-stimuli awareness.

Effects of Early Nutrition Factors on Baseline Neurodevelopment during the First 6 Months of Life: An EEG Study.

Throughout infancy, the brain undergoes rapid changes in structure and function that are sensitive to environmental influences, such as diet. Breastfed (BF) infants score higher on cognitive tests throughout infancy and into adolescence than formula fed (FF) infants, and these differences in neurocognitive development are reflected in higher concentrations of white and grey matter as measured by MRI. To further explore the effect diet has on cognitive development, electroencephalography (EEG) is used as a direct measure of neuronal activity and to assess specific frequency bands associated with cognitive processes. Task-free baseline EEGs were collected from infants fed with human milk (BF), dairy-based formula (MF), or soy-based formula (SF) at 2, 3, 4, 5, and 6 months of age to explore differences in frequency bands in both sensor and source space. Significant global differences in sensor space were seen in beta and gamma bands between BF and SF groups at ages 2 and 6 months, and these differences were further observed through volumetric modeling in source space. We conclude that BF infants exhibit earlier brain maturation reflected in greater power spectral density in these frequency bands.

Analysis of Caregiver Burden Expressed in Social Media Discussions.

Almost 40% of US adults provide informal caregiving, yet research gaps remain around what burdens affect informal caregivers. This study uses a novel social media site, Reddit, to mine and better understand what online communities focus on as their caregiving burdens. These forums were accessed using an application programming interface, a machine learning classifier was developed to remove low information posts, and topic modeling was applied to the corpus. An expert panel summarized the forums' themes into ten categories. The largest theme extracted from Reddit's forums discussed the personal emotional toll of being a caregiver. This was followed by logistic issues while caregiving and caring for parents who have cancer. Smaller themes included approaches to end-of-life care, physical equipment needs when caregiving, and the use of wearables or technology to help monitor care recipients. The platform often discusses caregiving for parents which may reflect the age of Reddit's users. This study confirms that Reddit forums are used for caregivers to discuss the burdens associated with their role and the types of stress that can result from informal caregiving.

Effect of Obesity on Arithmetic Processing in Preteens With High and Low Math Skills: An Event-Related Potentials Study.

Preadolescence is an important period for the consolidation of certain arithmetic facts, and the development of problem-solving strategies. Obese subjects seem to have poorer academic performance in math than their normal-weight peers, suggesting a negative effect of obesity on math skills in critical developmental periods. To test this hypothesis, event-related potentials (ERPs) were collected during a delayed-verification math task using simple addition and subtraction problems in obese [above 95th body mass index (BMI) percentile] and non-obese (between 5th and 90th BMI percentile) preteens with different levels of math skill; thirty-one with low math skills (14 obese, mean BMI = 26.40, 9.79 years old; 17 non-obese, BMI = 17.45, 9.76 years old) and thirty-one with high math skills (15 obese, BMI = 26.90, 9.60 years old; 16 non-obese, BMI = 17.13, 9.63 years old). No significant differences between weight groups were observed in task accuracy regardless of their mathematical skill level. For ERPs, electrophysiological differences were found only in the subtraction condition; participants with obesity showed an electrophysiologic pattern associated with a reduced ability to allocate attention resources regardless of their math skill level, these differences were characterized by longer P300 latency than their normal-weight peers. Moreover, the participants with obesity with high math skills displayed hypoactivity in left superior parietal lobule compared with their normal-weight peers. Additionally, obese preteens with low math skills displayed smaller arithmetic N400 amplitude than non-obese participants, reflecting difficulties in retrieving visual, semantic, and lexical information about numbers. We conclude that participants with obesity are less able than their normal-weight peers to deploy their attention regardless of their behavioral performance, which seems to have a greater effect on obese participants with low math skills because they also show problems in the retrieval of solutions from working memory, resulting in a delay in the development of mathematical skills.

Amplitude setting and dopamine response of finger tapping and gait are related in Parkinson's disease.

Movement amplitude setting is affected early in Parkinson's disease (PD), clinically manifesting as bradykinesia. Our objective was to determine if amplitude setting of upper limb bimanual movements and bipedal gait are similarly modulated in PD. 27 PD and 24 control participants were enrolled. Participants performed a bimanual anti-phase finger tapping task wearing gloves with joint angular sensors, and an instrumented gait assessment. Participants performed normal and fast paced assessments to vary motor load. PD participants were evaluated OFF (PD-OFF) and ON (PD-ON) levodopa. PD-OFF participants had smaller tap amplitude, and greater tap amplitude variability than controls in the more affected hands (all p < 0.05). Tap amplitude and stride length (p = 0.030) were correlated in PD-OFF. Tap amplitude was also correlated with motor UPDRS (p < 0.005) and bradykinesia motor (p < 0.05) and ADL (p < 0.005) UPDRS subscores. The relative amount of improvement in tap amplitude and stride length with levodopa was correlated. In PD, upper limb and gait amplitude setting are similarly scaled with motor demand and dopamine supplementation. This suggests these automated motor functions are subserved by common functional networks.

The use of rodent models to better characterize the relationship among epilepsy, sleep, and memory.

Epilepsy, a neurological disorder characterized by recurrent seizures, is known to be associated with impaired sleep and memory. Although the specific mechanisms underlying these impairments are uncertain, the known role of sleep in memory consolidation suggests a potential relationship may exist between seizure activity, disrupted sleep, and memory impairment. A possible mediator in this relationship is the sleep spindle, the characteristic electroencephalographic (EEG) feature of non-rapid-eye-movement (NREM) sleep in humans and other mammals. Growing evidence supports the idea that sleep spindles, having thalamic origin, may mediate the process of long-term memory storage and plasticity by generating neuronal conditions that favor these processes. To study this potential relationship, a single model in which memory, sleep, and epilepsy can be simultaneously observed is of necessity. Rodent models of epilepsy appear to fulfill this requirement. Not only do rodents express both sleep spindles and seizure-induced sleep disruptions, but they also allow researchers to invasively study neurobiological processes both pre- and post- epileptic onset via the artificial induction of epilepsy (a practice that cannot be carried out in human subjects). However, the degree to which sleep architecture differs between rodents and humans makes direct comparisons between the two challenging. This review addresses these challenges and concludes that rodent sleep studies are useful in observing the functional roles of sleep and how they are affected by epilepsy.

Feasibility of telemedicine research visits in people with Parkinson's disease residing in medically underserved areas.

Introduction: Gait, balance, and cognitive impairment make travel cumbersome for People with Parkinson's disease (PwPD). About 75% of PwPD cared for at the University of Arkansas for Medical Sciences' Movement Disorders Clinic reside in medically underserved areas (MUAs). Validated remote evaluations could help improve their access to care. Our goal was to explore the feasibility of telemedicine research visits for the evaluation of multi-modal function in PwPD in a rural state. Methods: In-home telemedicine research visits were performed in PwPD. Motor and non-motor disease features were evaluated and quantified by trained personnel, digital survey instruments for self-assessments, digital voice recordings, and scanned and digitized Archimedes spiral drawings. Participant's MUA residence was determined after evaluations were completed. Results: Twenty of the fifty PwPD enrolled resided in MUAs. The groups were well matched for disease duration, modified motor UPDRS, and Montreal Cognitive assessment scores but MUA participants were younger. Ninety-two percent were satisfied with their visit, and 61% were more likely to participate in future telemedicine research. MUA participants traveled longer distances, with higher travel costs, lower income, and education level. While 50% of MUA participants reported self-reliance for in-person visits, 85% reported self-reliance for the telemedicine visit. We rated audio-video quality highly in approximately 60% of visits in both groups. There was good correlation with prior in-person research assessments in a subset of participants. Conclusions: In-home research visits for PwPD in MUAs are feasible and could help improve access to care and research participation in these traditionally underrepresented populations.

The Human Connectome Project: A retrospective.

The Human Connectome Project (HCP) was launched in 2010 as an ambitious effort to accelerate advances in human neuroimaging, particularly for measures of brain connectivity; apply these advances to study a large number of healthy young adults; and freely share the data and tools with the scientific community. NIH awarded grants to two consortia; this retrospective focuses on the "WU-Minn-Ox" HCP consortium centered at Washington University, the University of Minnesota, and University of Oxford. In just over 6 years, the WU-Minn-Ox consortium succeeded in its core objectives by: 1) improving MR scanner hardware, pulse sequence design, and image reconstruction methods, 2) acquiring and analyzing multimodal MRI and MEG data of unprecedented quality together with behavioral measures from more than 1100 HCP participants, and 3) freely sharing the data (via the ConnectomeDB database) and associated analysis and visualization tools. To date, more than 27 Petabytes of data have been shared, and 1538 papers acknowledging HCP data use have been published. The "HCP-style" neuroimaging paradigm has emerged as a set of best-practice strategies for optimizing data acquisition and analysis. This article reviews the history of the HCP, including comments on key events and decisions associated with major project components. We discuss several scientific advances using HCP data, including improved cortical parcellations, analyses of connectivity based on functional and diffusion MRI, and analyses of brain-behavior relationships. We also touch upon our efforts to develop and share a variety of associated data processing and analysis tools along with detailed documentation, tutorials, and an educational course to train the next generation of neuroimagers. We conclude with a look forward at opportunities and challenges facing the human neuroimaging field from the perspective of the HCP consortium.

Virtual Reality in the Neurosciences: Current Practice and Future Directions.

Virtual reality has made numerous advancements in recent years and is used with increasing frequency for education, diversion, and distraction. Beginning several years ago as a device that produced an image with only a few pixels, virtual reality is now able to generate detailed, three-dimensional, and interactive images. Furthermore, these images can be used to provide quantitative data when acting as a simulator or a rehabilitation device. In this article, we aim to draw attention to these areas, as well as highlight the current settings in which virtual reality (VR) is being actively studied and implemented within the field of neurosurgery and the neurosciences. Additionally, we discuss the current limitations of the applications of virtual reality within various settings. This article includes areas in which virtual reality has been used in applications both inside and outside of the operating room, such as pain control, patient education and counseling, and rehabilitation. Virtual reality's utility in neurosurgery and the neurosciences is widely growing, and its use is quickly becoming an integral part of patient care, surgical training, operative planning, navigation, and rehabilitation.

Establishing a digital health platform in an academic medical center supporting rural communities.

The University of Arkansas for Medical Sciences (UAMS), like many rural states, faces clinical and research obstacles to which digital innovation is seen as a promising solution. To implement digital technology, a mobile health interest group was established to lay the foundation for an enterprise-wide digital health innovation platform. To create a foundation, an interprofessional team was established, and a series of formal networking events was conducted. Three online digital health training models were developed, and a full-day regional conference was held featuring nationally recognized speakers and panel discussions with clinicians, researchers, and patient advocates involved in digital health programs at UAMS. Finally, an institution-wide survey exploring the interest in and knowledge of digital health technologies was distributed. The networking events averaged 35-45 attendees. About 100 individuals attended the regional conference with positive feedback from participants. To evaluate mHealth knowledge at the institution, a survey was completed by 257 UAMS clinicians, researchers, and staff. It revealed that there are opportunities to increase training, communication, and collaboration for digital health implementation. The inclusion of the mobile health working group in the newly formed Institute for Digital Health and Innovation provides a nexus for healthcare providers and researches to facilitate translational research.

Gibbs distribution for statistical analysis of graphical data with a sample application to fcMRI brain images.

This paper develops object-oriented data analysis (OODA) statistical methods that are novel and complementary to existing methods of analysis of human brain scan connectomes, defined as graphs representing brain anatomical or functional connectivity. OODA is an emerging field where classical statistical approaches (e.g., hypothesis testing, regression, estimation, and confidence intervals) are applied to data objects such as graphs or functions. By analyzing data objects directly we avoid loss of information that occurs when data objects are transformed into numerical summary statistics. By providing statistical tools that analyze sets of connectomes without loss of information, new insights into neurology and medicine may be achieved. In this paper we derive the formula for statistical model fitting, regression, and mixture models; test their performance in simulation experiments; and apply them to connectomes from fMRI brain scans collected during a serial reaction time task study. Software for fitting graphical object-oriented data analysis is provided.

ConnectomeDB--Sharing human brain connectivity data.

ConnectomeDB is a database for housing and disseminating data about human brain structure, function, and connectivity, along with associated behavioral and demographic data. It is the main archive and dissemination platform for data collected under the WU-Minn consortium Human Connectome Project. Additional connectome-style study data is and will be made available in the database under current and future projects, including the Connectome Coordination Facility. The database currently includes multiple modalities of magnetic resonance imaging (MRI) and magnetoencephalograpy (MEG) data along with associated behavioral data. MRI modalities include structural, task, resting state and diffusion. MEG modalities include resting state and task. Imaging data includes unprocessed, minimally preprocessed and analysis data. Imaging data and much of the behavioral data are publicly available, subject to acceptance of data use terms, while access to some sensitive behavioral data is restricted to qualified investigators under a more stringent set of terms. ConnectomeDB is the public side of the WU-Minn HCP database platform. As such, it is geared towards public distribution, with a web-based user interface designed to guide users to the optimal set of data for their needs and a robust backend mechanism based on the commercial Aspera fasp service to enable high speed downloads. HCP data is also available via direct shipment of hard drives and Amazon S3.

Spatiotemporal analysis of the appearance of gamma-band Microstates in resting state MEG.

Spatiotemporal analysis of EEG signal has revealed a rich set of methods to quantify neuronal activity using spatially global topographic templates, called Microstates. These methods complement more traditional spectral analysis, which uses band limited source data to determine defining differences in band power and peak characteristics. The high sampling rate and increased resistance to high frequency noise of MEG data offers an opportunity to explore the utility of spatiotemporal analysis over a wider spectrum than in EEG. In this work, we explore the utility of representing band limited MEG source data using established microstate techniques, especially in gamma frequency bands - a range yet unexplored using these techniques. We develop methods for gauging the goodness-of-fit achieved by resultant microstate templates and demonstrate sensor-level dispersion characteristics across wide-band signals as well as across signals filtered by canonical bands. These analyses reveal that, while high-frequency-band derived microstate templates are visually lawful, they fail to exhibit important explained variance and dispersion characteristics present in low- and full-band data necessary to meet the requirements of a microstate model.

Cortical-subcortical interactions in hypersomnia disorders: mechanisms underlying cognitive and behavioral aspects of the sleep-wake cycle.

Subcortical circuits mediating sleep-wake functions have been well characterized in animal models, and corroborated by more recent human studies. Disruptions in these circuits have been identified in hypersomnia disorders (HDs) such as narcolepsy and Kleine-Levin Syndrome, as well as in neurodegenerative disorders expressing excessive daytime sleepiness. However, the behavioral expression of sleep-wake functions is not a simple on-or-off state determined by subcortical circuits, but encompasses a complex range of behaviors determined by the interaction between cortical networks and subcortical circuits. While conceived as disorders of sleep, HDs are equally disorders of wake, representing a fundamental instability in neural state characterized by lapses of alertness during wake. These episodic lapses in alertness and wakefulness are also frequently seen in neurodegenerative disorders where electroencephalogram demonstrates abnormal function in cortical regions associated with cognitive fluctuations (CFs). Moreover, functional connectivity MRI shows instability of cortical networks in individuals with CFs. We propose that the inability to stabilize neural state due to disruptions in the sleep-wake control networks is common to the sleep and cognitive dysfunctions seen in hypersomnia and neurodegenerative disorders.

Automated measurement of skull circumference, cranial index, and braincase volume from pediatric computed tomography.

Normative values of pediatric skull circumference, cranial index, and braincase volume would inform multiple disciplines including neurosurgery, plastic surgery and anthropology. Semi-automated methods exist for obtaining these data but are time consuming and require expertise. We report on a new method for automated extraction of in vivo measures of pediatric crania based on x-ray computed tomography scans (CT). Data were obtained from a clinical image repository for pediatric populations in whom no pathology was noted. The automated process showed good agreement with semi-automated measures, although there was a small bias for both braincase volume and circumference. We developed an open source program to automatically extract measures of skull circumference, cranial index, and braincase volume that are likely to prove useful in multiple disciplines.

Atypical resting-state functional connectivity of affective pain regions in chronic migraine.

OBJECTIVE: Chronic migraineurs (CM) have painful intolerances to somatosensory, visual, olfactory, and auditory stimuli during and between migraine attacks. These intolerances are suggestive of atypical affective responses to potentially noxious stimuli. We hypothesized that atypical resting-state functional connectivity (rs-fc) of affective pain-processing brain regions may associate with these intolerances. This study compared rs-fc of affective pain-processing regions in CM with controls. METHODS: Twelve minutes of resting-state blood oxygenation level-dependent data were collected from 20 interictal adult CM and 20 controls. Rs-fc between 5 affective regions (anterior cingulate cortex, right/left anterior insula, and right/left amygdala) with the rest of the brain was determined. Functional connections consistently differing between CM and controls were identified using summary analyses. Correlations between number of migraine years and the strengths of functional connections that consistently differed between CM and controls were calculated. RESULTS: Functional connections with affective pain regions that differed in CM and controls included regions in anterior insula, amygdala, pulvinar, mediodorsal thalamus, middle temporal cortex, and periaqueductal gray. There were significant correlations between the number of years with CM and functional connectivity strength between the anterior insula with mediodorsal thalamus and anterior insula with periaqueductal gray. CONCLUSION: CM is associated with interictal atypical rs-fc of affective pain regions with pain-facilitating and pain-inhibiting regions that participate in sensory-discriminative, cognitive, and integrative domains of the pain experience. Atypical rs-fc with affective pain regions may relate to aberrant affective pain processing and atypical affective responses to painful stimuli characteristic of CM.

Characterization of scale-free properties of human electrocorticography in awake and slow wave sleep States.

Like many complex dynamic systems, the brain exhibits scale-free dynamics that follow power-law scaling. Broadband power spectral density (PSD) of brain electrical activity exhibits state-dependent power-law scaling with a log frequency exponent that varies across frequency ranges. Widely divergent naturally occurring neural states, awake and slow wave sleep (SWS), were used to evaluate the nature of changes in scale-free indices of brain electrical activity. We demonstrate two analytic approaches to characterizing electrocorticographic (ECoG) data obtained during awake and SWS states. A data-driven approach was used, characterizing all available frequency ranges. Using an equal error state discriminator (EESD), a single frequency range did not best characterize state across data from all six subjects, though the ability to distinguish awake and SWS ECoG data in individual subjects was excellent. Multi-segment piecewise linear fits were used to characterize scale-free slopes across the entire frequency range (0.2-200 Hz). These scale-free slopes differed between awake and SWS states across subjects, particularly at frequencies below 10 Hz and showed little difference at frequencies above 70 Hz. A multivariate maximum likelihood analysis (MMLA) method using the multi-segment slope indices successfully categorized ECoG data in most subjects, though individual variation was seen. In exploring the differences between awake and SWS ECoG data, these analytic techniques show that no change in a single frequency range best characterizes differences between these two divergent biological states. With increasing computational tractability, the use of scale-free slope values to characterize ECoG and EEG data will have practical value in clinical and research studies.

Determination of neural state classification metrics from the power spectrum of human ECoG.

Brain electrical activity exhibits scale-free dynamics that follow power law scaling. Previous works have shown that broadband spectral power exhibits state-dependent scaling with a log frequency exponent that systematically varies with neural state. However, the frequency ranges which best characterize biological state are not consistent across brain location or subject. An adaptive piecewise linear fitting solution was developed to extract features for classification of brain state. Performance was evaluated by comparison to an a posteriori based feature search method. This analysis, using the 1/ƒ characteristics of the human ECoG signal, demonstrates utility in advancing the ability to perform automated brain state discrimination.