The impact of technology-based compensatory behaviors on subjective cognitive decline in older adults with a family history of dementia.

The current study examined whether greater use of technology to help with daily tasks is associated with less subjective cognitive decline (SCD), especially in individuals with a family history of Alzheimer's disease (AD). Individuals over the age of 50 (n = 102; age range 50-85) completed surveys about their digital and analog approaches to daily tasks, physical activity, and SCD. Participants with and without family histories of AD were matched on age, education, sex, and family history of AD using the R package MatchIt. There was no main effect of technology-based behavioral strategies on SCD (p = 0.259). However, a family history of AD moderated the association between technology use and SCD even when controlling for another protective lifestyle factor, physical activity. In individuals with a family history of AD, more reliance on technology-based behavioral strategies was associated with less SCD (p = 0.018), but this relationship was not significant in individuals without family history of AD (p = 0.511). Our findings suggest that technology-based behavioral strategies are associated with less SCD in individuals with a family history of AD, independent of another protective lifestyle factor. Future recommendations provided by healthcare providers to address SCD in cognitively unimpaired older adults might include focusing on technological assistance.

Feasibility and Validity of the Coma Recovery Scale-Revised for Accelerated Standardized Testing: A Practical Assessment Tool for Detecting Consciousness in the Intensive Care Unit.

We developed and validated an abbreviated version of the Coma Recovery Scale-Revised (CRS-R), the CRS-R For Accelerated Standardized Testing (CRSR-FAST), to detect conscious awareness in patients with severe traumatic brain injury in the intensive care unit. In 45 consecutively enrolled patients, CRSR-FAST administration time was approximately one-third of the full-length CRS-R (mean [SD] 6.5 [3.3] vs 20.1 [7.2] minutes, p < 0.0001). Concurrent validity (simple kappa 0.68), test-retest (Mak's ρ = 0.76), and interrater (Mak's ρ = 0.91) reliability were substantial. Sensitivity, specificity, and accuracy for detecting consciousness were 81%, 89%, and 84%, respectively. The CRSR-FAST facilitates serial assessment of consciousness, which is essential for diagnostic and prognostic accuracy. ANN NEUROL 2023;94:919-924.

Regional associations of white matter integrity and neurological, post-traumatic stress disorder and autonomic symptoms in Veterans with and without history of loss of consciousness in mild TBI.

Background: Posttraumatic stress disorder (PTSD) and mild traumatic brain injury (mTBI) share overlapping symptom presentations and are highly comorbid conditions among Veteran populations. Despite elevated presentations of PTSD after mTBI, mechanisms linking the two are unclear, although both have been associated with alterations in white matter and disruptions in autonomic regulation. The present study aimed to determine if there is regional variability in white matter correlates of symptom severity and autonomic functioning in a mixed sample of Veterans with and without PTSD and/or mTBI (N = 77). Methods: Diffusion-weighted images were processed to extract fractional anisotropy (FA) values for major white matter structures. The PTSD Checklist-Military version (PCL-M) and Neurobehavioral Symptom Inventory (NSI) were used to determine symptom domains within PTSD and mTBI. Autonomic function was assessed using continuous blood pressure and respiratory sinus arrythmia during a static, standing angle positional test. Mixed-effect models were used to assess the regional specificity of associations between symptom severity and white matter, with FA, global symptom severity (score), and white matter tract (tract) as predictors. Additional interaction terms of symptom domain (i.e., NSI and PCL-M subscales) and loss of consciousness (LoC) were added to evaluate potential moderating effects. A parallel analysis was conducted to explore concordance with autonomic functioning. Results: Results from the two-way Score × Tract interaction suggested that global symptom severity was associated with FA in the cingulum angular bundle (positive) and uncinate fasciculus (negative) only, without variability by symptom domain. We also found regional specificity in the relationship between FA and autonomic function, such that FA was positively associated with autonomic function in all tracts except the cingulum angular bundle. History of LoC moderated the association for both global symptom severity and autonomic function. Conclusions: Our findings are consistent with previous literature suggesting that there is significant overlap in the symptom presentation in TBI and PTSD, and white matter variability associated with LoC in mTBI may be associated with increased PTSD-spectra symptoms. Further research on treatment response in patients with both mTBI history and PTSD incorporating imaging and autonomic assessment may be valuable in understanding the role of brain injury in treatment outcomes and inform treatment design.

Psychometric properties of the Autonomic Symptoms Checklist in the Lewy body disease module of the uniform dataset.

INTRODUCTION: Autonomic dysfunction is an important feature of Lewy Body Dementia (DLB), but measurement of autonomic symptoms has been limited in both previous research and clinical practice. Accurate measurement of autonomic dysfunction has the potential to improve our understanding of the course and progression of DLB, given that autonomic symptoms typically precede cognitive impairment and are associated with functional impairment. The primary aim of this study was to examine the psychometric properties of the two versions (3.0 and 3.1) of the NACC LBD-module Autonomic Symptom Checklist (ASC). METHODS: Psychometric analyses of the ASC (internal consistency, reliability, factor structure, and validity) were conducted on data acquired from 245 individuals with DLB from the NACC database. ASC V3.0 was contrasted on these attributes to V3.1. RESULTS: Results suggested an underlying factor structure for the ASC, and confirmatory factor analysis (CFA) revealed 3 factors, which generally aligned with discrete autonomic systems. The ASC V3.0 and CFA-identified scales were comparable in terms of reliability, which were both improved relative to the ASC V3.1. In terms of ecological validity, CFA-identified items related to gastrointestinal/thermoregulation symptoms were significantly more associated with functional outcomes compared to the unitary ASC. CONCLUSION: Findings underscore the importance of differentiation within the autonomic system. Future research into autonomic symptom classes and lab-based pathophysiological measurement of autonomic dysfunction in DLB has the potential to support early identification and inform treatment planning.

Central Executive Network and Executive Function in Patients With Alzheimer's Disease and Healthy Individuals: Meta-Analysis of Structural and Functional MRI.

OBJECTIVE: The neural architecture of executive function is of interest given its utility as a transdiagnostic predictor of adaptive functioning. However, a gap exists in the meta-analytic literature assessing this relationship in neuropsychiatric populations, concordance between structural and functional architecture, and the relationship with neuropsychological assessment of executive function. Given the importance of the central executive network (CEN) in Alzheimer's disease, this population may be useful in understanding this relationship in Alzheimer's disease pathology. METHODS: A meta-analysis of studies (k=21) was conducted to elucidate the relationship between executive function and CEN for structural architecture (k=10; N=1,027) among patients with Alzheimer's disease (k=6; N=250) and healthy control subjects (HCs) (k=4; N=777) and for functional architecture (k=11; N=522) among patients with Alzheimer's disease (k=6; N=306) and HCs (k=5; N=216). Random-effects modeling was used to increase accuracy of conclusions about population means. RESULTS: Analyses revealed a positive brain-behavior relationship (pr=0.032, 95% CI=0.07, 0.54), although there was a lack of statistically significant heterogeneity between functional and structural neuroimaging (Q=9.89, p=0.971, I2=0.00%) and between the Alzheimer's and HC groups in functional (Q=8.18, p=0.612, I2=0.00%) and structural (Q=1.60, p=0.996, I2=0.00%) neuroimaging. Similarly, a lack of statistically significant heterogeneity was revealed between functional and structural neuroimaging among patients with Alzheimer's disease (Q=3.59, p=0.980, I2=0.00%) and HCs (Q=3.67, p=0.885, I2=0.00%). CONCLUSIONS: Structural and functional imaging in the CEN are predictive of executive function performance among patients with Alzheimer's disease and HCs. Regardless of how the CEN is affected, behavior is correlated to the degree to which the CEN is affected. Findings are significant in the context of methodological decisions in multimodal neuroimaging research.

The central executive network and executive function in healthy and persons with schizophrenia groups: a meta-analysis of structural and functional MRI.

This meta-analysis evaluated the extent to which executive function can be understood with structural and functional magnetic resonance imaging. Studies included structural in schizophrenia (k = 8; n = 241) and healthy controls (k = 12; n = 1660), and functional in schizophrenia (k = 4; n = 104) and healthy controls (k = 12; n = 712). Results revealed a positive association in the brain behavior relationship when pooled across schizophrenia and control samples for structural (pr = 0.27) and functional (pr = 0.29) modalities. Subgroup analyses revealed no significant difference for functional neuroimaging (pr = .43, 95%CI = -.08-.77, p = .088) but with structural neuroimaging (pr = .37, 95%CI = -.08-.69, p = .015) the association to executive functions is lower in the control group. Subgroup analyses also revealed no significant differences in the strength of the brain-behavior relationship in the schizophrenia group (pr = .59, 95%CI = .58-.61, p = .881) or the control group (pr = 0.19, 95%CI = 0.18-0.19, p = 0.920), suggesting concordance.

Snowball ICA: A Model Order Free Independent Component Analysis Strategy for Functional Magnetic Resonance Imaging Data.

In independent component analysis (ICA), the selection of model order (i.e., number of components to be extracted) has crucial effects on functional magnetic resonance imaging (fMRI) brain network analysis. Model order selection (MOS) algorithms have been used to determine the number of estimated components. However, simulations show that even when the model order equals the number of simulated signal sources, traditional ICA algorithms may misestimate the spatial maps of the signal sources. In principle, increasing model order will consider more potential information in the estimation, and should therefore produce more accurate results. However, this strategy may not work for fMRI because large-scale networks are widely spatially distributed and thus have increased mutual information with noise. As such, conventional ICA algorithms with high model orders may not extract these components at all. This conflict makes the selection of model order a problem. We present a new strategy for model order free ICA, called Snowball ICA, that obviates these issues. The algorithm collects all information for each network from fMRI data without the limitations of network scale. Using simulations and in vivo resting-state fMRI data, our results show that component estimation using Snowball ICA is more accurate than traditional ICA. The Snowball ICA software is available at https://github.com/GHu-DUT/Snowball-ICA.

White matter connectometry among individuals with self-reported family history of drug and alcohol use disorders.

Heredity is an important risk factor for alcoholism. Several studies have been conducted on small groups of alcohol naïve adolescents which show lowered fractional anisotropy of frontal white matter in individuals with a family history of alcohol and substance use disorder (FH+). We compare large adult FH+ and FH- groups using white matter connectometry, different from the previously used global tractography method, as it is more sensitive to regional variability. Imaging and behavioral data from the Human Connectome Project (WU-MINN HCP 1200) was analyzed. Groups of participants were positive (n = 109) and negative (n = 109) for self-reported alcohol and substance use disorders in at least one parent, and stringently matched. Connectometry was performed on diffusion MRI in DSI-Studio using q-space diffeomorphic reconstruction, and multiple regression was completed with 5000 permutations. Analyses showed decreased major tract (>40 mm) connectivity in the FH+ group in left inferior longitudinal fasciculus, bilateral cortico-striatal pathway, left cortico-thalamic pathway, and corpus callosum, compared to the FH- group. For cognitive tasks related to reward processing, inhibition, and monitoring, there were a number of interactions, such that the relationship between identified tracts and behavior differed significantly between groups. Self-reported family history was associated with decreased connectivity in reward signaling pathways, controlling for alcohol consumption and alcohol use disorder. This is the first connectometry study of FH+, and extends the neural basis of the hereditary diathesis of alcoholism beyond that demonstrated with global tractography. Regions associated with FH+ are similar to those associated with alcohol use disorder.

Components of executive function model regional prefrontal volumes.

OBJECTIVE: Designed to measure a diversity of executive functioning (EF) through classical neuropsychological tests, the Delis-Kaplan Executive Function Scale (D-KEFS) allows for the investigation of the neural architecture of EF. We examined how the D-KEFS Tower, Verbal Fluency, Design Fluency, Color-Word Interference, and Trail Making Test tasks related to frontal lobe volumes, quantifying the regional specificity of EF components. METHOD: Adults from the Nathan Kline Institute-Rockland Sample (NKI-RS), an open-access community study of brain development, with complete MRI (3T scanner) and D-KEFS data were selected for analysis (N = 478; ages 20-85). In a mixed-effects model predicting volume, D-KEFS task, D-KEFS score, region of interest (ROI; 13 frontal, 1 occipital control), were entered as fixed effects with intercepts for participants as random effects. RESULTS: "Unitary" EF (aggregate of D-KEFS scores) was positively associated with superior frontal, rostral middle frontal, and lateral orbitofrontal volumes; a negative association was observed with frontal pole volume (| z-score slope | range = 0.040 to 0.051). "Diverse" EF skills (individual D-KEFS task scores) were differentially associated with two or three ROIs, respectively, but to a stronger extent (| z-score slope | range = 0.053 to 0.103). CONCLUSIONS: The neural correlates found for the D-KEFS support the prefrontal modularity of both unitary (aspects of EF ability common to all tasks) and diverse EF. Our findings contribute to emerging evidence that aggregate measurements of EF may serve broader but less robust frontal neural correlates than distinct EF skills. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Tensor clustering on outer-product of coefficient and component matrices of independent component analysis for reliable functional magnetic resonance imaging data decomposition.

BACKGROUND: Stability of spatial components is frequently used as a post-hoc selection criteria for choosing the dimensionality of an independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data. Although the stability of the ICA temporal courses differs from that of spatial components, temporal stability has not been considered during dimensionality decisions. NEW METHOD: The current study aims to (1) develop an algorithm to incorporate temporal course stability into dimensionality selection and (2) test the impact of temporal course on the stability of the ICA decomposition of fMRI data via tensor clustering. Resting state fMRI data were analyzed with two popular ICA algorithms, InfomaxICA and FastICA, using our new method and results were compared with model order selection based on spatial or temporal criteria alone. RESULTS: Hierarchical clustering indicated that the stability of the ICA decomposition incorporating spatiotemporal tensor information performed similarly when compared to current best practice. However, we found that component spatiotemporal stability and convergence of the model varied significantly with model order. Considering both may lead to methodological improvements for determining ICA model order. Selected components were also significantly associated with relevant behavioral variables. Comparison with Existing Method: The Kullback-Leibler information criterion algorithm suggests the optimal model order for group ICA is 40, compared to the proposed method with an optimal model order of 20. CONCLUSION: The current study sheds new light on the importance of temporal course variability in ICA of fMRI data.

Identifying errors in Freesurfer automated skull stripping and the incremental utility of manual intervention.

Quality assurance (QA) is vital for ensuring the integrity of processed neuroimaging data for use in clinical neurosciences research. Manual QA (visual inspection) of processed brains for cortical surface reconstruction errors is resource-intensive, particularly with large datasets. Several semi-automated QA tools use quantitative detection of subjects for editing based on outlier brain regions. There were two project goals: (1) evaluate the assumption that statistical outliers are related to errors of cortical extension, and (2) examine whether error identification and correction significantly impacts estimation of cortical parameters and established brain-behavior relationships. T1 MPRAGE images (N = 530) of healthy adults were obtained from the NKI-Rockland Sample and reconstructed using Freesurfer 5.3. Visual inspection of T1 images was conducted for: (1) participants (n = 110) with outlier values (z scores ±3 SD) for subcortical and cortical segmentation volumes (outlier group), and (2) a random sample of remaining participants (n = 110) with segmentation values that did not meet the outlier criterion (non-outlier group). The outlier group had 21% more participants with visual inspection-identified errors than participants in the non-outlier group, with a medium effect size (Φ = 0.22). Nevertheless, a considerable portion of images with errors of cortical extension were found in the non-outlier group (41%). Although nine brain regions significantly changed size from pre- to post-editing (with effect sizes ranging from 0.26 to 0.59), editing did not substantially change the correlations of neurocognitive tasks and brain volumes (ps > 0.05). Statistically-based QA, although less resource intensive, is not accurate enough to supplant visual inspection. We discuss practical implications of our findings to guide resource allocation decisions for image processing.

On the impact of interhemispheric white matter: Age, executive functioning, and dedifferentiation in the frontal lobes.

INTRODUCTION: In middle age, declines in executive functioning (EF) are associated with decrements in the quality and/or quantity of white and grey matter. Recruitment of homologous regions has been identified as a compensatory mechanism for cognitive decline in later middle age; however, research into neural substrates of EF has yet to be guided by dedifferentiation models. We hypothesized that frontal-parietal grey matter volume, interhemispheric white matter, and intrahemispheric white matter fractional anisotropy will be predictive of EF. Further, we hypothesized that the comparative association between interhemispheric white matter and EF will increase with age, because of compensatory recruitment. METHODS: Neurocognitive test data, DTI, and T1 MPRAGE scans (n = 444) were obtained from the NKI-Rockland Sample. Structural equation modeling was used to examine the relationship between age, EF, interhemispheric white matter (forceps minor; FM), intrahemispheric white matter (superior longitudinal fasciculus; SLF), and a frontal-parietal grey matter network. EF and grey matter were modelled as latent variables, with EF examined as the criterion. Additionally, a subsample of participants aged 55 to 85 (n = 168) was analyzed to examine the influence of age related compensatory mechanisms. RESULTS: There was a significant relationship between FM, grey matter, and EF, which was fully mediated by age. There was a significant relationship between SLF and EF, which was not mediated by age. For older adults, only the age-mediated pathway from FM to EF was significant. DISCUSSION: Using structural imaging data, support was found for age-related interhemispheric mechanisms of compensation, but not intrahemispheric mechanisms.

The real-time fMRI neurofeedback based stratification of Default Network Regulation Neuroimaging data repository.

This data descriptor describes a repository of openly shared data from an experiment to assess inter-individual differences in default mode network (DMN) activity. This repository includes cross-sectional functional magnetic resonance imaging (fMRI) data from the Multi Source Interference Task, to assess DMN deactivation, the Moral Dilemma Task, to assess DMN activation, a resting state fMRI scan, and a DMN neurofeedback paradigm, to assess DMN modulation, along with accompanying behavioral and cognitive measures. We report technical validation from n=125 participants of the final targeted sample of 180 participants. Each session includes acquisition of one whole-brain anatomical scan and whole-brain echo-planar imaging (EPI) scans, acquired during the aforementioned tasks and resting state. The data includes several self-report measures related to perseverative thinking, emotion regulation, and imaginative processes, along with a behavioral measure of rapid visual information processing. Technical validation of the data confirms that the tasks deactivate and activate the DMN as expected. Group level analysis of the neurofeedback data indicates that the participants are able to modulate their DMN with considerable inter-subject variability. Preliminary analysis of behavioral responses and specifically self-reported sleep indicate that as many as 73 participants may need to be excluded from an analysis depending on the hypothesis being tested. The present data are linked to the enhanced Nathan Kline Institute, Rockland Sample and builds on the comprehensive neuroimaging and deep phenotyping available therein. As limited information is presently available about individual differences in the capacity to directly modulate the default mode network, these data provide a unique opportunity to examine DMN modulation ability in relation to numerous phenotypic characteristics.