While delirium is associated with cognitive decline and dementia, there is limited evidence to support causality for this relationship. Clarification of how delirium may cause cognitive decline, perhaps through evidence of contemporaneous neuronal injury, would enhance plausibility for a causal relationship. Dose-dependence of neuronal injury with delirium severity would further enhance the biological plausibility for this relationship. We tested whether delirium is associated with neuronal injury in 114 surgical patients recruited to a prospective biomarker cohort study. Patients underwent perioperative testing for changes in neurofilament light, a neuronal injury biomarker, as well as a panel of 10 cytokines, with contemporaneous assessment of delirium severity and incidence. A subset of patients underwent preoperative MRI. Initially we confirmed prior reports that neurofilament light levels correlated with markers of neurodegeneration [hippocampal volume (ΔR2 = 0.129, P = 0.015)] and white matter changes including fractional anisotropy of white matter (ΔR2 = 0.417, P < 0.001) with similar effects on mean, axial and radial diffusivity) in our cohort and that surgery was associated with increasing neurofilament light from preoperative levels [mean difference (95% confidence interval, CI) = 0.240 (0.178, 0.301) log10 (pg/ml), P < 0.001], suggesting putative neuronal injury. Next, we tested the relationship with delirium. Neurofilament light rose more sharply in participants with delirium compared to non-sufferers [mean difference (95% CI) = 0.251 (0.136, 0.367) log10 (pg/ml), P < 0.001]. This relationship showed dose-dependence, such that neurofilament light rose proportionately to delirium severity (ΔR2 = 0.199, P < 0.001). Given that inflammation is considered an important driver of postoperative delirium, next we tested whether neurofilament light, as a potential marker of neurotoxicity, may contribute to the pathogenesis of delirium independent of inflammation. From a panel of 10 cytokines, the pro-inflammatory cytokine IL-8 exhibited a strong correlation with delirium severity (ΔR2 = 0.208, P < 0.001). Therefore, we tested whether the change in neurofilament light contributed to delirium severity independent of IL-8. Neurofilament light was independently associated with delirium severity after adjusting for the change in inflammation (ΔR2 = 0.040, P = 0.038). These data suggest delirium is associated with exaggerated increases in neurofilament light and that this putative neurotoxicity may contribute to the pathogenesis of delirium itself, independent of changes in inflammation.

Background: Delirium frequently affects older patients, increasing morbidity and mortality; however, the pathogenesis is poorly understood. Herein, we tested the cognitive disintegration model, which proposes that a breakdown in frontoparietal connectivity, provoked by increased slow-wave activity (SWA), causes delirium. Methods: We recruited 70 surgical patients to have preoperative and postoperative cognitive testing, EEG, blood biomarkers, and preoperative MRI. To provide evidence for causality, any putative mechanism had to differentiate on the diagnosis of delirium; change proportionally to delirium severity; and correlate with a known precipitant for delirium, inflammation. Analyses were adjusted for multiple corrections (MCs) where appropriate. Results: In the preoperative period, subjects who subsequently incurred postoperative delirium had higher alpha power, increased alpha band connectivity (MC P<0.05), but impaired structural connectivity (increased radial diffusivity; MC P<0.05) on diffusion tensor imaging. These connectivity effects were correlated (r 2 ¼0.491; P¼0.0012). Postoperatively, local SWA over frontal cortex was insufficient to cause delirium. Rather, delirium was associated with increased SWA involving occipitoparietal and frontal cortex, with an accompanying breakdown in functional connectivity. Changes in connectivity correlated with SWA (r 2 ¼0.257; P<0.0001), delirium severity rating (r 2 ¼0.195; P<0.001), interleukin 10 (r 2 ¼0.152; P¼0.008), and monocyte chemoattractant protein 1 (r 2 ¼0.253; P<0.001). Conclusions: Whilst frontal SWA occurs in all postoperative patients, delirium results when SWA progresses to involve posterior brain regions, with an associated reduction in connectivity in most subjects. Modifying SWA and connectivity may offer a novel therapeutic approach for delirium. Clinical trial registration: NCT03124303, NCT02926417

Loss of motor function is a common deficit following stroke insult and often manifests as persistent upper extremity (UE) disability which can affect a survivor’s ability to participate in activities of daily living. Recent research suggests the use of brain–computer interface (BCI) devices might improve UE function in stroke survivors at various times since stroke. This randomized crossover-controlled trial examines whether intervention with this BCI device design attenuates the effects of hemiparesis, encourages reorganization of motor related brain signals (EEG measured sensorimotor rhythm desynchronization), and improves movement, as measured by the Action Research Arm Test (ARAT). A sample of 21 stroke survivors, presenting with varied times since stroke and levels of UE impairment, received a maximum of 18–30 h of intervention with a novel electroencephalogram-based BCI-driven functional electrical stimulator (EEG-BCI-FES) device. Driven by spectral power recordings from contralateral EEG electrodes during cued attempted grasping of the hand, the user’s input to the EEG-BCI-FES device modulates horizontal movement of a virtual cursor and also facilitates concurrent stimulation of the impaired UE. Outcome measures of function and capacity were assessed at baseline, mid-therapy, and at completion of therapy while EEG was recorded only during intervention sessions. A significant increase in r-squared values [reflecting Mu rhythm (8–12 Hz) desynchronization as the result of attempted movements of the impaired hand] presented post-therapy compared to baseline. These findings suggest that intervention corresponds with greater desynchronization of Mu rhythm in the ipsilesional hemisphere during attempted movements of the impaired hand and this change is related to changes in behavior as a result of the intervention. BCI intervention may be an effective way of addressing the recovery of a stroke impaired UE and studying neuromechanical coupling with motor outputs.Clinical Trial Registration: ClinicalTrials.gov, identifier NCT02098265.

Functional magnetic resonance imaging (fMRI)-based functional connectivity (FC) commonly characterizes the functional connections in the brain. Conventional quantification of FC by Pearson's correlation captures linear, time-domain dependencies among blood-oxygen-level-dependent (BOLD) signals. We examined measures to quantify FC by investigating: (i) Is Pearson's correlation sufficient to characterize FC? (ii) Can alternative measures better quantify FC? (iii) What are the implications of using alternative FC measures? FMRI analysis in healthy adult population suggested that: (i) Pearson's correlation cannot comprehensively capture BOLD inter-dependencies. (ii) Eight alternative FC measures were similarly consistent between task and resting-state fMRI, improved age-based classification and provided better association with behavioral outcomes. (iii) Formulated hypotheses were: first, in lieu of Pearson's correlation, an augmented, composite and multi-metric definition of FC is more appropriate; second, canonical large-scale brain networks may depend on the chosen FC measure. A thorough notion of FC promises better understanding of variations within a given population.open Scientific RepoRtS | (2020) 10:1298 | https://doi.org/10.1038/s41598-020-57915-wwww.nature.com/scientificreports www.nature.com/scientificreports/ dependencies). These different measures were compared and contrasted with three experiments which assessed the consistency of information provided by the FC measures, utility of the FC measures in population-based discrimination and the biological plausibility of using these FC measures.Experiment 1 (E1). The goal of the first experiment was to understand the relative consistency of the different FC measures when evaluated in young and healthy individuals. Within-individual consistency was first established by comparing the overlap (Sørensen-Dice similarity coefficient) of the FC pattern observed in task (motor and verbal) and resting-state functional MRI and then averaging across individuals for each FC measure. Findings from E1: Consistency of functional connectivity. Consistency of all of the identified FCmeasures was evaluated by comparing task functional MRI and resting-state functional MRI in young healthy adults (demographics in Table 1). Since the connectivity pattern may be different for each FC measure, the task functional MRI data offer a form of ground truth as they activate specific regions in the brain which can bear correspondence with those in resting condition. Thresholded FC maps (threshold = one standard deviation above grand mean) between task and resting-state conditions are illustrated for Pearson's correlation in Fig. 2 (subfigure A) and remaining FC measures in Supplementary Fig. 1.Consistency of resting-state FC, quantified by Sørensen-Dice similarity coefficient, is tabulated in Table 2 for the motor and language networks. While mostly comparable across many FC measures, the overlap coefficients for Pearson's correlation are not necessarily the best in any of these tested net...

Biological subtypes in Alzheimer’s disease, originally identified on neuropathological data, have been translated to in vivo biomarkers such as structural magnetic resonance imaging (sMRI) and positron emission tomography (PET), to disentangle the heterogeneity within Alzheimer’s disease. Although there is methodological variability across studies, comparable characteristics of subtypes are reported at the group level. In this study, we investigated whether group-level similarities translate to individual-level agreement across subtyping methods, in a head-to-head context. We compared five previously published subtyping methods. Firstly, we validated the subtyping methods in 89 amyloid-beta positive Alzheimer’s disease dementia patients (reference group: 70 amyloid-beta negative healthy individuals) using sMRI. Secondly, we extended and applied the subtyping methods to 53 amyloid-beta positive prodromal Alzheimer’s disease and 30 amyloid-beta positive Alzheimer’s disease dementia patients (reference group: 200 amyloid-beta negative healthy individuals) using sMRI and tau PET. Subtyping methods were implemented as outlined in each original study. Group-level and individual-level comparisons across methods were performed. Each individual subtyping method was replicated, and the proof-of-concept was established. At the group level, all methods captured subtypes with similar patterns of demographic and clinical characteristics, and with similar cortical thinning and tau PET uptake patterns. However, at the individual level large disagreements were found in subtype assignments. Although characteristics of subtypes are comparable at the group level, there is a large disagreement at the individual level across subtyping methods. Therefore, there is an urgent need for consensus and harmonization across subtyping methods. We call for establishment of an open benchmarking framework to overcome this problem.

Stroke is a leading cause of persistent upper extremity (UE) motor disability in adults. Brain–computer interface (BCI) intervention has demonstrated potential as a motor rehabilitation strategy for stroke survivors. This sub-analysis of ongoing clinical trial (NCT02098265) examines rehabilitative efficacy of this BCI design and seeks to identify stroke participant characteristics associated with behavioral improvement. Stroke participants (n = 21) with UE impairment were assessed using Action Research Arm Test (ARAT) and measures of function. Nine participants completed three assessments during the experimental BCI intervention period and at 1-month follow-up. Twelve other participants first completed three assessments over a parallel time-matched control period and then crossed over into the BCI intervention condition 1-month later. Participants who realized positive change (≥1 point) in total ARAT performance of the stroke affected UE between the first and third assessments of the intervention period were dichotomized as “responders” (<1 = “non-responders”) and similarly analyzed. Of the 14 participants with room for ARAT improvement, 64% (9/14) showed some positive change at completion and approximately 43% (6/14) of the participants had changes of minimal detectable change (MDC = 3 pts) or minimally clinical important difference (MCID = 5.7 points). Participants with room for improvement in the primary outcome measure made significant mean gains in ARATtotal score at completion (ΔARATtotal = 2, p = 0.028) and 1-month follow-up (ΔARATtotal = 3.4, p = 0.0010), controlling for severity, gender, chronicity, and concordance. Secondary outcome measures, SISmobility, SISadl, SISstrength, and 9HPTaffected, also showed significant improvement over time during intervention. Participants in intervention through follow-up showed a significantly increased improvement rate in SISstrength compared to controls (p = 0.0117), controlling for severity, chronicity, gender, as well as the individual effects of time and intervention type. Participants who best responded to BCI intervention, as evaluated by ARAT score improvement, showed significantly increased outcome values through completion and follow-up for SISmobility (p = 0.0002, p = 0.002) and SISstrength (p = 0.04995, p = 0.0483). These findings may suggest possible secondary outcome measure patterns indicative of increased improvement resulting from this BCI intervention regimen as well as demonstrating primary efficacy of this BCI design for treatment of UE impairment in stroke survivors.Clinical Trial Registration: ClinicalTrials.gov, NCT02098265.

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Interventional therapy using brain-computer interface (BCI) technology has shown promise in facilitating motor recovery in stroke survivors; however, the impact of this form of intervention on functional networks outside of the motor network specifically is not well-understood. Here, we investigated resting-state functional connectivity (rs-FC) in stroke participants undergoing BCI therapy across stages, namely pre- and post-intervention, to identify discriminative functional changes using a machine learning classifier with the goal of categorizing participants into one of the two therapy stages. Twenty chronic stroke participants with persistent upper-extremity motor impairment received neuromodulatory training using a closed-loop neurofeedback BCI device, and rs-functional MRI (rs-fMRI) scans were collected at four time points: pre-, mid-, post-, and 1 month post-therapy. To evaluate the peak effects of this intervention, rs-FC was analyzed from two specific stages, namely pre- and post-therapy. In total, 236 seeds spanning both motor and non-motor regions of the brain were computed at each stage. A univariate feature selection was applied to reduce the number of features followed by a principal component-based data transformation used by a linear binary support vector machine (SVM) classifier to classify each participant into a therapy stage. The SVM classifier achieved a cross-validation accuracy of 92.5% using a leave-one-out method. Outside of the motor network, seeds from the fronto-parietal task control, default mode, subcortical, and visual networks emerged as important contributors to the classification. Furthermore, a higher number of functional changes were observed to be strengthening from the pre- to post-therapy stage than the ones weakening, both of which involved motor and non-motor regions of the brain. These findings may provide new evidence to support the potential clinical utility of BCI therapy as a form of stroke rehabilitation that not only benefits motor recovery but also facilitates recovery in other brain networks. Moreover, delineation of stronger and weaker changes may inform more optimal designs of BCI interventional therapy so as to facilitate strengthened and suppress weakened changes in the recovery process.