Diffusion tensor imaging of superficial prefrontal white matter in healthy aging

The prefrontal cortex (PFC) is a heterogenous structure that is highly susceptible to the effects of aging. Few studies have investigated age effects on the superficial white matter (WM) contained within the PFC using in-vivo magnetic resonance imaging (MRI). This study used diffusion tensor imaging (DTI) tractography to examine the effects of age, sex, and intracranial volume (ICV) on superficial WM within specific PFC subregions, and to model the relationships with age using higher order polynomial regression modelling. PFC WM of 140 healthy individuals, aged 18-85, was segmented into medial and lateral orbitofrontal, medial prefrontal, and dorsolateral prefrontal subregions. Differences due to age in microstructural parameters such as fractional anisotropy (FA), axial and radial diffusivities, and macrostructural measures of tract volumes, fiber counts, average fiber lengths, and average number of fibers per voxel were examined. We found that most prefrontal subregions demonstrated age effects, with decreases in FA, tract volume, and fiber counts, and increases in all diffusivity measures. Age relationships were mostly non-linear, with higher order regressions chosen in most cases. Declines in PFC FA began at the onset of adulthood while the greatest changes in diffusivity and volume did not occur until middle age. The effects of age were most prominent in medial tracts while the lateral orbitofrontal tracts were less affected. Significant effects of sex and ICV were also observed in certain parameters. The patterns mostly followed myelination order, with late-myelinating prefrontal subregions experiencing earlier and more pronounced age effects, further supporting the frontal theory of aging.

Cross-covariance isolate detect: A new change-point method for estimating dynamic functional connectivity

Evidence of the non stationary behavior of functional connectivity (FC) networks has been observed in task based functional magnetic resonance imaging (fMRI) experiments and even prominently in resting state fMRI data. This has led to the development of several new statistical methods for estimating this time-varying connectivity, with the majority of the methods utilizing a sliding window approach. While computationally feasible, the sliding window approach has several limitations. In this paper, we circumvent the sliding window, by introducing a statistical method that finds change-points in FC networks where the number and location of change-points are unknown a priori. The new method, called cross-covariance isolate detect (CCID), detects multiple change-points in the second-order (cross-covariance or network) structure of multivariate, possibly high-dimensional time series. CCID allows for change-point detection in the presence of frequent changes of possibly small magnitudes, can assign change-points to one or multiple brain regions, and is computationally fast. In addition, CCID is particularly suited to task based data, where the subject alternates between task and rest, as it firstly attempts isolation of each of the change-points within subintervals, and secondly their detection therein. Furthermore, we also propose a new information criterion for CCID to identify the change-points. We apply CCID to several simulated data sets and to task based and resting state fMRI data and compare it to recent change-point methods. CCID may also be applicable to electroencephalography (EEG), magentoencephalography (MEG) and electrocorticography (ECoG) data. Similar to other biological networks, understanding the complex network organization and functional dynamics of the brain can lead to profound clinical implications. Finally, the R package ccid implementing the method from the paper is available from CRAN.

Investigating the effects of healthy cognitive aging on brain functional connectivity using 4.7 T resting-state functional magnetic resonance imaging

Functional changes in the aging human brain have been previously reported using functional magnetic resonance imaging (fMRI). Earlier resting-state fMRI studies revealed an age-associated weakening of intra-system functional connectivity (FC) and age-associated strengthening of inter-system FC. However, the majority of such FC studies did not investigate the relationship between age and network amplitude, without which correlation-based measures of FC can be challenging to interpret. Consequently, the main aim of this study was to investigate how three primary measures of resting-state fMRI signal-network amplitude, network topography, and inter-network FC-are affected by healthy cognitive aging. We acquired resting-state fMRI data on a 4.7 T scanner for 105 healthy participants representing the entire adult lifespan (18-85 years of age). To study age differences in network structure, we combined ICA-based network decomposition with sparse graphical models. Older adults displayed lower blood-oxygen-level-dependent (BOLD) signal amplitude in all functional systems, with sensorimotor networks showing the largest age differences. Our age comparisons of network topography and inter-network FC demonstrated a substantial amount of age invariance in the brain's functional architecture. Despite architecture similarities, old adults displayed a loss of communication efficiency in our inter-network FC comparisons, driven primarily by the FC reduction in frontal and parietal association cortices. Together, our results provide a comprehensive overview of age effects on fMRI-based FC.

Mental Health Symptoms Unexpectedly Increased in Students Aged 11-19 Years During the 3.5 Years After the 2016 Fort McMurray Wildfire: Findings From 9,376 Survey Responses

In Fort McMurray, Alberta, Canada, the wildfire of May 2016 forced the population of 88,000 to rapidly evacuate in a traumatic and chaotic manner. Ten percentage of the homes in the city were destroyed, and many more structures were damaged. Since youth are particularly vulnerable to negative effects of natural disasters, we examined possible long-term psychological impacts. To assess this, we partnered with Fort McMurray Public and Catholic Schools, who surveyed Grade 7-12 students (aged 11-19) in November 2017, 2018, and 2019-i.e., at 1.5, 2.5, and 3.5 years after the wildfire. The survey included validated measurement scales for post-traumatic stress disorder (PTSD), depression, anxiety, drug use, alcohol use, tobacco use, quality of life, self-esteem, and resilience. Data analysis was done on large-scale anonymous surveys including 3,070 samples in 2017; 3,265 samples in 2018; and 3,041 samples in 2019. The results were unexpected and showed that all mental health symptoms increased from 2017 to 2019, with the exception of tobacco use. Consistent with this pattern, self-esteem and quality of life scores decreased. Resilience scores did not change significantly. Thus, mental health measures worsened, in contrast to our initial hypothesis that they would improve over time. Of note, we observed higher levels of mental health distress among older students, in females compared to male students, and in individuals with a minority gender identity, including transgender and gender-non-conforming individuals. These findings demonstrate that deleterious mental health effects can persist in youth for years following a wildfire disaster. This highlights the need for multi-year mental health support programs for youth in post-disaster situations. The indication that multi-year, post-disaster support is warranted is relatively novel, although not unknown. There is a need to systematically investigate factors associated with youth recovery following a wildfire disaster, as well as efficacy of psychosocial strategies during later phases of disaster recovery relative to early post-disaster interventions.

Generalized reliability based on distances

The intraclass correlation coefficient (ICC) is a classical index of measurement reliability. With the advent of new and complex types of data for which the ICC is not defined, there is a need for new ways to assess reliability. To meet this need, we propose a new distance-based ICC (dbICC), defined in terms of arbitrary distances among observations. We introduce a bias correction to improve the coverage of bootstrap confidence intervals for the dbICC, and demonstrate its efficacy via simulation. We illustrate the proposed method by analyzing the test-retest reliability of brain connectivity matrices derived from a set of repeated functional magnetic resonance imaging scans. The Spearman-Brown formula, which shows how more intensive measurement increases reliability, is extended to encompass the dbICC.

After the Fort McMurray wildfire there are significant increases in mental health symptoms in grade 7-12 students compared to controls

BACKGROUND

In order to examine the impact of disasters on adolescent mental health, this study compared population mental health survey data from two communities in Alberta, Canada: Fort McMurray, which experienced a major natural disaster, and Red Deer, which did not.

METHODS

Data from 3070 grade 7-12 students from Fort McMurray, Alberta, Canada (collected in 2017, 18 months after the 2016 wildfire) was compared with data from 2796 grade 7-12 students from Red Deer, Alberta, Canada (collected in 2014). The same measurement scales were used for both surveys. Both of these cities have populations of approximately 100,000, and both cities are located in Alberta, Canada. For this reason, Red Deer is an appropriate non-disaster impacted community to compare to the disaster impacted community of Fort McMurray.

RESULTS

The results of this comparison demonstrate that mental health symptoms were statistically significantly elevated in the Fort McMurray population when compared to the control population in Red Deer. This occurred for scores consistent with a diagnosis of depression (31% vs. 17%), moderately severe depression (17% vs. 9%), suicidal thinking (16% vs. 4%), and tobacco use (13% vs. 10%). Consistent with there being major mental health impacts from the 2016 Fort McMurray wildfire, self-esteem scores and quality of life scores were also statistically significantly lower in Fort McMurray. While the rates of anxiety disorder were similar (15% vs. 16%), the mean scores on the anxiety scale were slightly higher, with this difference reaching statistical significance. There were no statistical differences in the rates or scores for alcohol or substance use.

CONCLUSIONS

Our results are consistent with previous findings showing a significant negative impact of disasters on many aspects of adolescent mental, with a particular increase in symptoms related to depression and suicidal thinking. These findings highlight first, the need to identify adolescents most at risk of developing psychiatric symptoms after experiencing the trauma of disaster and second, the importance and necessity of implementing short and long term mental health intervention programs specifically aimed at adolescents, in order to help mitigate the negative effects of disasters on their mental health.

Significant PTSD and Other Mental Health Effects Present 18 Months After the Fort Mcmurray Wildfire: Findings From 3,070 Grades 7-12 Students

Background: The May 2016 wildfire in Fort McMurray, Alberta, Canada forced evacuation of the population of 88,000 individuals and destroyed 10% of the homes. Youth are particularly impacted by disaster. Methods: Eighteen months after the wildfire, Fort McMurray Public and Catholic Schools surveyed 3,252 of the 4,407 students in Grades 7-12 to determine possible long-term psychological impacts. The survey included validated measurement scales for post-traumatic stress disorder (PTSD), depression, anxiety, use of drugs, alcohol, and tobacco, quality of life, self-esteem, and resilience. Data analysis was possible for only 3,070 students, i.e., 70% of the total student population. Anonymized data were analyzed to compare students who directly experienced lesser or greater impact from the wildfire, with greater impact defined as personally seeing the fire or having one's home destroyed. Also, students with greater or lesser scores on the Child and Youth Resilience Measure (CYRM-12) were compared. Results: Of the 3,070 students, 37% met criteria for probable PTSD; 31% met criteria for probable depression, and 17% for probable depression of at least moderate severity; 27% of students met criteria for probable anxiety, and 15% for probable alcohol or substance use disorder; 46% of all students met criteria for one or more probable diagnosis of PTSD, depression, anxiety, or alcohol/substance abuse, and this included students who were both present and not present in Fort McMurray at the time of the wildfire. Students with greater impact from the wildfire exhibited significantly higher scores on measures of PTSD, depression, anxiety, and alcohol/substance use. They also had lower self-esteem and quality of life scores. Students with lower resilience scores exhibited a similar pattern. Conclusions: These findings highlight first the negative impact of disasters on youth mental health, particularly for those who directly experience wildfire, and second the role of resilience on youth mental health, with lower resilience associated with substantially lower mental health outcomes. These results emphasize the need for long-term mental health supports for youth post-disaster, with specific focus on increasing youth resilience, which may serve as a protective factor against effects of disaster on mental health.

A longitudinal model for functional connectivity networks using resting-state fMRI

Many neuroimaging studies collect functional magnetic resonance imaging (fMRI) data in a longitudinal manner. However, the current fMRI literature lacks a general framework for analyzing functional connectivity (FC) networks in fMRI data obtained from a longitudinal study. In this work, we build a novel longitudinal FC model using a variance components approach. First, for all subjects' visits, we account for the autocorrelation inherent in the fMRI time series data using a non-parametric technique. Second, we use a generalized least squares approach to estimate 1) the within-subject variance component shared across the population, 2) the baseline FC strength, and 3) the FC's longitudinal trend. Our novel method for longitudinal FC networks seeks to account for the within-subject dependence across multiple visits, the variability due to the subjects being sampled from a population, and the autocorrelation present in fMRI time series data, while restricting the number of parameters in order to make the method computationally feasible and stable. We develop a permutation testing procedure to draw valid inference on group differences in the baseline FC network and change in FC over longitudinal time between a set of patients and a comparable set of controls. To examine performance, we run a series of simulations and apply the model to longitudinal fMRI data collected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Overall, we found no difference in the global FC network between Alzheimer's disease patients and healthy controls, but did find differing local aging patterns in the FC between the left hippocampus and the posterior cingulate cortex.

Sparse Graphical Models for Functional Connectivity Networks: Best Methods and the Autocorrelation Issue

Sparse graphical models are frequently used to explore both static and dynamic functional brain networks from neuroimaging data. However, the practical performance of the models has not been studied in detail for brain networks. In this work, we have two objectives. First, we compare several sparse graphical model estimation procedures and several selection criteria under various experimental settings, such as different dimensions, sample sizes, types of data, and sparsity levels of the true model structures. We discuss in detail the superiority and deficiency of each combination. Second, in the same simulation study, we show the impact of autocorrelation and whitening on the estimation of functional brain networks. We apply the methods to a resting-state functional magnetic resonance imaging (fMRI) data set. Our results show that the best sparse graphical model, in terms of detection of true connections and having few false-positive connections, is the smoothly clipped absolute deviation (SCAD) estimating method in combination with the Bayesian information criterion (BIC) and cross-validation (CV) selection method. In addition, the presence of autocorrelation in the data adversely affects the estimation of networks but can be helped by using the CV selection method. These results question the validity of a number of fMRI studies where inferior graphical model techniques have been used to estimate brain networks.

Sparse Graphical Models for Functional Connectivity Networks: Best Methods and the Autocorrelation Issue

Sparse graphical models are frequently used to explore both static and dynamic functional brain networks from neuroimaging data. However, the practical performance of the models has not been studied in detail for brain networks. In this work, we have two objectives. First, we compare several sparse graphical model estimation procedures and several selection criteria under various experimental settings, such as different dimensions, sample sizes, types of data, and sparsity levels of the true model structures. We discuss in detail the superiority and deficiency of each combination. Second, in the same simulation study, we show the impact of autocorrelation and whitening on the estimation of functional brain networks. We apply the methods to a resting-state functional magnetic resonance imaging (fMRI) data set. Our results show that the best sparse graphical model, in terms of detection of true connections and having few false-positive connections, is the smoothly clipped absolute deviation (SCAD) estimating method in combination with the Bayesian information criterion (BIC) and cross-validation (CV) selection method. In addition, the presence of autocorrelation in the data adversely affects the estimation of networks but can be helped by using the CV selection method. These results question the validity of a number of fMRI studies where inferior graphical model techniques have been used to estimate brain networks.

Changes in brain activity following intensive voice treatment in children with cerebral palsy

Eight children (3 females; 8-16 years) with motor speech disorders secondary to cerebral palsy underwent 4 weeks of an intensive neuroplasticity-principled voice treatment protocol, LSVT LOUD^® , followed by a structured 12-week maintenance program. Children were asked to overtly produce phonation (ah) at conversational loudness, cued-phonation at perceived twice-conversational loudness, a series of single words, and a prosodic imitation task while being scanned using fMRI, immediately pre- and post-treatment and 12 weeks following a maintenance program. Eight age- and sex-matched controls were scanned at each of the same three time points. Based on the speech and language literature, 16 bilateral regions of interest were selected a priori to detect potential neural changes following treatment. Reduced neural activity in the motor areas (decreased motor system effort) before and immediately after treatment, and increased activity in the anterior cingulate gyrus after treatment (increased contribution of decision making processes) were observed in the group with cerebral palsy compared to the control group. Using graphical models, post-treatment changes in connectivity were observed between the left supramarginal gyrus and the right supramarginal gyrus and the left precentral gyrus for the children with cerebral palsy, suggesting LSVT LOUD enhanced contributions of the feedback system in the speech production network instead of high reliance on feedforward control system and the somatosensory target map for regulating vocal effort. Network pruning indicates greater processing efficiency and the recruitment of the auditory and somatosensory feedback control systems following intensive treatment. Hum Brain Mapp 38:4413-4429, 2017. © 2017 Wiley Periodicals, Inc.

Long-term Results from the Empowering a Multimodal Pathway Toward Healthy Youth Program, a Multimodal School-Based Approach, Show Marked Reductions in Suicidality, Depression, and Anxiety in 6,227 Students in Grades 6-12 (Aged 11-18)

Here, we report on findings from a 15-month follow-up of a school-based program called Empowering a Multimodal Pathway Toward Healthy Youth (EMPATHY). This was primarily intended to reduce suicidal thinking in pre-teens, adolescents, and youth students aged 11-18 in middle schools (Grades 6-8) and high SCHOOLS (Grades 9-12). It also aimed to reduce depression and anxiety. The EMPATHY multimodal program consisted of repeated data collection, identification of a high-risk group, a rapid intervention for this high-risk group including offering supervised online cognitive behavioral therapy (CBT) program, a universal CBT intervention for those in Grades 6-8, a variety of interactions with trained staff ("Resiliency Coaches"), and referral to external medical and psychiatric services where appropriate. There were four time-points at which assessments were made: baseline, 3, 7, and 15 months. Here, we report cross-sectional findings over 15 months in a total of 6,227 students who were assessed at least once during the study period. Additionally, we report longitudinal findings from the 1,884 students who completed all 4 assessments. Our results found highly statistically significant decreases in suicidality rates, with the percentage of the total school population who were actively suicidal decreasing from 4.4% at baseline (n = 143 of 3,244) to 2.8% at 15 months (n = 125 of 4,496) (p < 0.001). There were also highly statistically significant reductions in depression and anxiety scores at each time-point. Thus, Mean Depression scores at baseline for the entire student population were 3.73 ± 3.87 (n = 3,244) at baseline and decreased to 3.22 ± 3.52 (n = 4,496) (p < 0.001). Since most students were not depressed, whole population changes such as this may indicate impact in many areas. In the longitudinal analysis of students who completed all four assessments, there were also highly statistically significant improvements in depression and anxiety scores at all time-points. For example, depression scores decreased from a mean of 3.43 ± 3.67 (n = 1,884) at baseline to 2.95 ± 3.53 (n = 1,884) at 15-months (p < 0.001), while the number who were actively suicidal decreased from 69 to 37. These results suggest that school-based multimodal programs, utilizing a combination of interventions, can have meaningful benefits across entire school populations.

A variance components model for statistical inference on functional connectivity networks

We propose a variance components linear modeling framework to conduct statistical inference on functional connectivity networks that directly accounts for the temporal autocorrelation inherent in functional magnetic resonance imaging (fMRI) time series data and for the heterogeneity across subjects in the study. The novel method estimates the autocorrelation structure in a nonparametric and subject-specific manner, and estimates the variance due to the heterogeneity using iterative least squares. We apply the new model to a resting-state fMRI study to compare the functional connectivity networks in both typical and reading impaired young adults in order to characterize the resting state networks that are related to reading processes. We also compare the performance of our model to other methods of statistical inference on functional connectivity networks that do not account for the temporal autocorrelation or heterogeneity across the subjects using simulated data, and show that by accounting for these sources of variation and covariation results in more powerful tests for statistical inference.

Depression Outcomes in Adults Attending Family Practice Were Not Improved by Screening, Stepped-Care, or Online CBT during a 12-Week Study when Compared to Controls in a Randomized Trial

There is uncertainty regarding possible benefits of screening for depression in family practice, as well as the most effective treatment approach when depression is identified. Here, we examined whether screening patients for depression in primary care, and then treating them with different modalities, was better than treatment-as-usual (TAU) alone. Screening was carried out for depression using the 9-item Patient Health Questionnaire (PHQ-9), with a score of ≥10 indicating significant depressive symptoms. PHQ-9 scores were given to family physicians prior to patients being seen (except for the Control group). Patients (n = 1,489) were randomized to one of four groups. Group #1 were controls (n = 432) in which PHQ-9 was administered, but results were not shared. Group #2 was screening followed by TAU (n = 426). Group #3 was screening followed by both TAU and the opportunity to use an online cognitive behavioral therapy (CBT) treatment program (n = 440). Group #4 utilized an evidence-based Stepped-care pathway for depression (n = 191, note that this was not available at all clinics). Of the study sample 889 (60%) completed a second PHQ-9 rating at 12 weeks. There were no statistically significant differences in baseline PHQ-9 scores between these groups. Compared to baseline, mean PHQ-9 scores decreased significantly in the depressed patients over 12 weeks, but there were no statistically significant differences between any groups at 12 weeks. Thus, for those who were depressed at baseline Control group (Group #1) scores decreased from 15.3 ± 4.2 to 4.0 ± 2.6 (p < 0.001), Screening group (Group #2) scores decreased from 15.5 ± 3.9 to 4.6 ± 3.0 (p < 0.001), Online CBT group (Group #3) scores decreased from 15.4 ± 3.8 to 3.4 ± 2.7 (p < 0.01), and the Stepped-care pathway group (Group #4) scores decreased from 15.3 ± 3.6 to 5.4 ± 2.8 (p < 0.05). In conclusion, these findings from this controlled randomized study do not suggest that using depression screening tools in family practice improves outcomes. They also suggest that much of the depression seen in primary care spontaneously resolves and do not support suggestions that more complex treatment programs or pathways improve depression outcomes in primary care. Replication studies are required due to study limitations.

Imaging Imageability: Behavioral Effects and Neural Correlates of Its Interaction with Affect and Context

The construct of imageability refers to the extent to which a word evokes a tangible sensation. Previous research (Westbury et al., 2013) suggests that the behavioral effects attributed to a word's imageability can be largely or wholly explained by two objective constructs, contextual density and estimated affect. Here, we extend these previous findings in two ways. First, we show that closely matched stimuli on the three measures of contextual density, estimated affect, and human-judged imageability show a three-way interaction in explaining variance in LD RTs, but that imagebility accounts for no additional variance after contextual density and estimated affect are entered first. Secondly, we demonstrate that the loci and functional connectivity (via graphical models) of the brain regions implicated in processing the three variables during that task are largely over-lapping and similar. These two lines of evidence support the conclusion that the effect usually attributed to human-judged imageability is largely or entirely due to the effects of other correlated measures that are directly computable.

Detecting functional connectivity change points for single-subject fMRI data

Recently in functional magnetic resonance imaging (fMRI) studies there has been an increased interest in understanding the dynamic manner in which brain regions communicate with one another, as subjects perform a set of experimental tasks or as their psychological state changes. Dynamic Connectivity Regression (DCR) is a data-driven technique used for detecting temporal change points in functional connectivity between brain regions where the number and location of the change points are unknown a priori. After finding the change points, DCR estimates a graph or set of relationships between the brain regions for data that falls between pairs of change points. In previous work, the method was predominantly validated using multi-subject data. In this paper, we concentrate on single-subject data and introduce a new DCR algorithm. The new algorithm increases accuracy for individual subject data with a small number of observations and reduces the number of false positives in the estimated undirected graphs. We also introduce a new Likelihood Ratio test for comparing sparse graphs across (or within) subjects; thus allowing us to determine whether data should be combined across subjects. We perform an extensive simulation analysis on vector autoregression (VAR) data as well as to an fMRI data set from a study (n = 23) of a state anxiety induction using a socially evaluative threat challenge. The focus on single-subject data allows us to study the variation between individuals and may provide us with a deeper knowledge of the workings of the brain.

Dynamic connectivity regression: determining state-related changes in brain connectivity

Most statistical analyses of fMRI data assume that the nature, timing and duration of the psychological processes being studied are known. However, often it is hard to specify this information a priori. In this work we introduce a data-driven technique for partitioning the experimental time course into distinct temporal intervals with different multivariate functional connectivity patterns between a set of regions of interest (ROIs). The technique, called Dynamic Connectivity Regression (DCR), detects temporal change points in functional connectivity and estimates a graph, or set of relationships between ROIs, for data in the temporal partition that falls between pairs of change points. Hence, DCR allows for estimation of both the time of change in connectivity and the connectivity graph for each partition, without requiring prior knowledge of the nature of the experimental design. Permutation and bootstrapping methods are used to perform inference on the change points. The method is applied to various simulated data sets as well as to an fMRI data set from a study (N=26) of a state anxiety induction using a socially evaluative threat challenge. The results illustrate the method's ability to observe how the networks between different brain regions changed with subjects' emotional state.