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Abstract Background: Suicide is the second leading cause of death for adolescents in the United States. However, relatively little is known about the forms of atypical neuro-cognitive function that are correlates of suicidal ideation (SI). One form of cognitive/affective function that, when dysfunctional, is associated with SI is emotion regulation. However, very little work has investigated the neural correlates of emotion dysregulation in adolescents with SI. Methods: Participants (N = 111 aged 12-18, 32 females, 31 [27.9%] reporting SI) were recruited shortly after their arrival at a residential care facility where they had been referred for behavioral and mental health problems. Daily reports of SI were collected during the participants’ first 90-days in residential care. Participants were presented with a task-fMRI measure of emotion regulation – the Affective Number Stroop task shortly after recruitment. Participants were divided into two groups matched for age, sex and IQ based on whether they demonstrated SI. Results: Participants who demonstrated SI showed increased recruitment of regions including dorsomedial prefrontal cortex/supplemental motor area and parietal cortex during task (congruent and incongruent) relative to view trials in the context of emotional relative to neutral distracters. Conclusions: Participants with SI showed increased recruitment of regions implicated in executive control during the performance of a task indexing automatic emotion regulation. Such data might suggest a relative inefficiency in the recruitment of these regions in individuals with SI.

Abstract Background Conduct disorder (CD) involves a group of behavioral and emotional problems that usually begins during childhood or adolescence. Structural brain alterations have been observed in CD, including the amygdala, insula, ventrolateral and medial prefrontal cortex, anterior cingulate cortex, and fusiform gyrus. The current study developed a multivariate generalized linear model (GLM) to differentiate adolescents with CD from typically developing (TD) adolescents in terms of grey matter volume (GMV). Methods The whole‐brain structural MRI data were collected from 96 adolescents with CD (mean age = $$16.188\pm 1.259$$ 16.188 ± 1.259 years; mean IQ = $$104.292\pm 8.107$$ 104.292 ± 8.107 ; 63 males) and 90 TD individuals (mean age = $$15.956\pm 1.506$$ 15.956 ± 1.506 years; mean IQ = $$106.622\pm 9.076$$ 106.622 ± 9.076 ; 59 males) matched on age, IQ, and sex. Region-wise GMV was extracted following whole-brain parcellation into 68 cortical and 14 subcortical regions for each participant. A multivariate GLM was developed to predict the GMV of the pre-hypothesized regions-of-interest (ROIs) based on CD diagnosis, with intracranial volume, age, sex, and IQ serving as the covariate. Results A diagnosis of CD was a significant predictor for GMV in the right pars orbitalis, right insula, right superior temporal gyrus, left fusiform gyrus, and left amygdala (F (1, 180) = 5.460–10.317, p

Abstract Suicide is the third leading cause of death for individuals between 15 and 19 years of age. The high suicide mortality rate and limited prior success in identifying neuroimaging biomarkers indicate that it is crucial to improve the accuracy of clinical neural signatures underlying suicide risk. The current study implements machine-learning (ML) algorithms to examine structural brain alterations in adolescents that can discriminate individuals with suicide risk from typically developing (TD) adolescents at the individual level. Structural MRI data were collected from 79 adolescents who demonstrated clinical levels of suicide risk and 79 demographically matched TD adolescents. Region-specific cortical/subcortical volume (CV/SCV) was evaluated following whole-brain parcellation into 1000 cortical and 12 subcortical regions. CV/SCV parameters were used as inputs for feature selection and three ML algorithms (i.e., support vector machine [SVM], K-nearest neighbors, and ensemble) to classify adolescents at suicide risk from TD adolescents. The highest classification accuracy of 74.79% (with sensitivity = 75.90%, specificity = 74.07%, and area under the receiver operating characteristic curve = 87.18%) was obtained for CV/SCV data using the SVM classifier. Identified bilateral regions that contributed to the classification mainly included reduced CV within the frontal and temporal cortices but increased volume within the cuneus/precuneus for adolescents at suicide risk relative to TD adolescents. The current data demonstrate an unbiased region-specific ML framework to effectively assess the structural biomarkers of suicide risk. Future studies with larger sample sizes and the inclusion of clinical controls and independent validation data sets are needed to confirm our findings.

Introduction: Depressive symptoms can emerge as early as childhood and may lead to adverse situations in adulthood. Studies have examined structural brain alternations in individuals with depressive symptoms, but findings remain inconclusive. Furthermore, previous studies have focused on adults or used a categorical approach to assess depression. The current study looks to identify grey matter volumes (GMV) that predict depressive symptomatology across a clinically concerning sample of adolescents. Methods: Structural MRI data were collected from 338 clinically concerning adolescents (mean age = 15.30 SD=2.07; mean IQ = 101.01 SD=12.43; 132 F). Depression symptoms were indexed via the Mood and Feelings Questionnaire (MFQ). Freesurfer was used to parcellate the brain into 68 cortical regions and 14 subcortical regions. GMV was extracted from all 82 brain areas. Multiple linear regression was used to look at the relationship between MFQ scores and region-specific GMV parameter. Follow up regressions were conducted to look at potential effects of psychiatric diagnoses and medication intake. Results: Our regression analysis produced a significant model (R2 = 0.446, F(86, 251) = 2.348, p

Suicide is a leading cause of death in the United States. Historically, scientific inquiry has focused on psychological theory. However, more recent studies have started to shed light on complex biosignatures using MRI techniques, including task-based and resting-state functional MRI, brain morphometry, and diffusion tensor imaging. Here, we review recent research across these modalities, with a focus on participants with depression and Suicidal Thoughts and Behavior (STB). A PubMed search identified 149 articles specific to our population of study, and this was further refined to rule out more diffuse pathologies such as psychotic disorders and organic brain injury and illness. This left 69 articles which are reviewed in the current study. The collated articles reviewed point to a complex impairment showing atypical functional activation in areas associated with perception of reward, social/affective stimuli, top-down control, and reward-based learning. This is broadly supported by the atypical morphometric and diffusion-weighted alterations and, most significantly, in the network-based resting-state functional connectivity data that extrapolates network functions from well validated psychological paradigms using functional MRI analysis. We see an emerging picture of cognitive dysfunction evident in task-based and resting state fMRI and network neuroscience studies, likely preceded by structural changes best demonstrated in morphometric and diffusion-weighted studies. We propose a clinically-oriented chronology of the diathesis-stress model of suicide and link other areas of research that may be useful to the practicing clinician, while helping to advance the translational study of the neurobiology of suicide.

Abstract Previous studies examining structural brain correlates of irritability have taken a region-specific approach and have been relatively inconsistent. In a sample of adolescents with and without clinically impairing irritability, the current study examines: (i) cortical volume (CV) in canonical functional networks; (ii) the association between the CV of functional networks and severity of irritability; and (iii) the extent to which IQ mediates the association between structural abnormalities and severity of irritability. Structural MRI and IQ data were collected from 130 adolescents with high irritability (mean age = 15.54±1.83 years, 58 females, self-reported Affective Reactivity Index [ARI] ≥ 4) and 119 adolescents with low irritability (mean age = 15.10±1.93 years, 39 females, self-reported ARI < 4). Subject-specific network-wise CV was estimated after parcellating the whole brain into 17 previously reported functional networks. Our Multivariate Analysis of Covariance (MANCOVA) revealed that adolescents with high irritability had significantly reduced CV of the bilateral control and default-mode networks (p

Background: The current study aimed to address two gaps in the literature on child maltreatment, reinforcement processing and psychopathology. First, the extent to which compromised reinforcement processing might be particularly associated with either neglect or abuse. Second, the extent to which maltreatment-related compromised reinforcement processing might be associated with particular symptom sets (depression, conduct problems, anxiety) or symptomatology more generally. Methods: A sample of adolescents (N = 142) aged between 14 and 18 years with varying levels of prior maltreatment participated in this fMRI study. They were scanned while performing a passive avoidance learning task, where the participant learns to respond to stimuli that engender reward and avoid responding to stimuli that engender punishment. Maltreatment (abuse and neglect) levels were assessed with the Childhood Trauma Questionnaire (CTQ). Results: We found that: (i) level of neglect, but not abuse, was negatively associated with differential BOLD responses to reward-punishment within the striatum and medial frontal cortex; and (ii) differential reward-punishment responses within these neglect-associated regions were particularly negatively associated with level of conduct problems. Conclusion: Our findings demonstrate the adverse neurodevelopmental impact of childhood maltreatment, particularly neglect, on reinforcement processing. Moreover, they suggest a neurodevelopmental route by which neglect might increase the risk for conduct problems.

Background: Irritability and callous-unemotional (CU; reduced guilt/empathy) traits vary dimensionally in the typically developing population but may be particularly marked in youth with conduct disorder (CD). While these dimensional traits are positively correlated, they have been associated with divergent forms of dysfunction, particularly with respect to threat processing (i.e., irritability with increased, and CU traits with decreased, threat responsiveness). This suggests that interactions between these two dimensions may be complex at the neurobiological level. However, this issue has received minimal empirical attention.Methods: The study included 105 adolescents (typically developing and cases with CD; N = 59). They were scanned with fMRI during a looming threat task that involved images of threatening and neutral human faces or animals that appeared to be either looming or receding.Results: Significant irritability-by-CU traits-by-Direction-by-Emotion interactions were seen within right thalamus/PAG, left lingual gyrus and right fusiform gyrus; irritability was positively associated with the BOLD response for Looming Threatening vs. Receding Threatening trials, particularly for youth with low CU traits. In contrast, CU traits were negatively associated with the same differential BOLD response but particularly for youth showing higher levels of irritability. Similar findings were seen within left ventral anterior and posterior cingulate cortices, though the addition of the interaction with CU traits was only seen at slightly more lenient thresholds.Conclusions: The results support previous work linking irritability to increased, and CU traits to reduced, threat responsiveness. However, for adolescents with high irritability, if CU traits are also high, the underlying neuropathology appears to relate to reduced, rather than increased, threat responsiveness.

Background: Alcohol and cannabis are commonly used by adolescents in the United States. Both alcohol use disorder (AUD) and cannabis use disorder (CUD) have been associated with reduced emotion expression recognition ability. However, this work has primarily occurred in adults and has not considered neuro-cognitive risk factors associated with conduct problems that commonly co-occur with, and precede, substance use. Yet, conduct problems are also associated with reduced emotion expression recognition ability. The current study investigated the extent of negative association between AUD and CUD symptom severity and expression recognition ability over and above any association of expression recognition ability with conduct problems [conduct disorder (CD) diagnostic status].Methods: In this study, 152 youths aged 12.5–18 years (56 female; 60 diagnosed with CD) completed a rapid presentation morphed intensity facial expression task to investigate the association between relative severity of AUD/CUD and expression recognition ability.Results: Cannabis use disorder identification test (CUDIT) scores were negatively associated with recognition accuracy for higher intensity (particularly sad and fearful) expressions while CD diagnostic status was independently negatively associated with recognition of sad expressions. Alcohol use disorder identification test (AUDIT) scores were not significantly associated with expression recognition ability.Conclusions: These data indicate that relative severity of CUD and CD diagnostic status are statistically independently associated with reduced expression recognition ability. On the basis of these data, we speculate that increased cannabis use during adolescence may exacerbate a neuro-cognitive risk factor for the emergence of aggression and antisocial behavior.

Background: Emerging evidence suggests that post concussive symptoms, including mood changes, may be improved through morning blue-wavelength light therapy (BLT). However, the neurobiological mechanisms underlying these effects remain unknown. We hypothesize that BLT may influence the effective brain connectivity (EC) patterns within the default-mode network (DMN), particularly involving the medial prefrontal cortex (MPFC), which may contribute to improvements in mood. Methods: Resting-state functional MRI data were collected from 41 healthy-controls (HCs) and 28 individuals with mild traumatic brain injury (mTBI). Individuals with mTBI also underwent a diffusion-weighted imaging scan and were randomly assigned to complete either 6 weeks of daily morning BLT (N = 14) or amber light therapy (ALT; N = 14). Advanced spectral dynamic causal modeling (sDCM) and diffusion MRI connectometry were used to estimate EC patterns and structural connectivity strength within the DMN, respectively. Results: The sDCM analysis showed dominant connectivity pattern following mTBI (pre-treatment) within the hemisphere contralateral to the one observed for HCs. BLT, but not ALT, resulted in improved directional information flow (ie, EC) from the left lateral parietal cortex (LLPC) to MPFC within the DMN. The improvement in EC from LLPC to MPFC was accompanied by stronger structural connectivity between the 2 areas. For the BLT group, the observed improvements in function and structure were correlated (at a trend level) with changes in self-reported happiness. Conclusions: The current preliminary findings provide empirical evidence that morning short-wavelength light therapy could be used as a novel alternative rehabilitation technique for mTBI. Trial registry: The research protocols were registered in the ClinicalTrials.gov database (CT Identifiers NCT01747811 and NCT01721356).

Introduction: Emotional Intelligence (EI) is a well-documented aspect of social and interpersonal functioning, but the underlying neural mechanisms for this capacity remain poorly understood. Here we used advanced brain connectivity techniques to explore the associations between EI and effective connectivity (EC) within four functional brain networks. Methods: The Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT) was used to collect EI data from 55 healthy individuals (mean age = 30.56±8.3 years, 26 males). The MSCEIT comprises two area cores – experiential EI (T1) and strategic EI (T2). The T1 core included two sub-scales – perception of emotions (S1) and using emotions to facilitate thinking (S2), and the T2 core included two sub-scales – understanding of emotions (S3) and management of emotions (S4). All participants underwent structural and resting-state functional magnetic resonance imaging (rsfMRI) scans. The spectral dynamic causal modeling approach was implemented to estimate EC within four networks of interest – the default-mode network (DMN), dorsal attention network (DAN), control-execution network (CEN) and salience network (SN). The strength of EC within each network was correlated with the measures of EI, with correlations at pFDR < 0.05 considered as significant. Results: There was no significant association between any of the measures of EI and EC strength within the DMN and DAN. For CEN, however, we found that there were significant negative associations between EC strength from the right anterior prefrontal cortex (RAPFC) to the left anterior prefrontal cortex (LAPFC) and both S2 and T1, and significant positive associations between EC strength from LAPFC to RAPFC and S2. EC strength from the right superior parietal cortex (SPC) to RAPFC also showed significant negative association with S4 and T2. For the SN, S3 showed significant negative association with EC strength from the right insula to RAPFC and significant positive association with EC strength from the left insula to dorsal anterior cingulate cortex (DACC). Conclusions: We provide evidence that the negative ECs within the right hemisphere, and from the right to left hemisphere, and positive ECs within the left hemisphere and from the left to right hemisphere of CEN (involving bilateral frontal and right parietal region) and SN (involving right frontal, anterior cingulate and bilateral insula) play a significant role in regulating and processing emotions. These findings also suggest that measures of EC can be utilized as important biomarkers to better understand the underlying neural mechanisms of EI.

Two of the most commonly used illegal substances by adolescents are alcohol and cannabis. Alcohol use disorder (AUD) and cannabis use disorder (CUD) are associated with poorer decision-making in adolescents. In adolescents, level of AUD symptomatology has been negatively associated with striatal reward responsivity. However, little work has explored the relationship with striatal reward prediction error (RPE) representation and the extent to which any augmentation of RPE by novel stimuli is impacted. One-hundred fifty-one adolescents participated in the Novelty Task while undergoing functional magnetic resonance imaging (fMRI). In this task, participants learn to choose novel or non-novel stimuli to gain monetary reward. Level of AUD symptomatology was negatively associated with both optimal decision-making and BOLD response modulation by RPE within striatum and regions of prefrontal cortex. The neural alterations in RPE representation were particularly pronounced when participants were exploring novel stimuli. Level of CUD symptomatology moderated the relationship between novelty propensity and RPE representation within inferior parietal lobule and dorsomedial prefrontal cortex. These data expand on an emerging literature investigating individual associations of AUD symptomatology levels versus CUD symptomatology levels and RPE representation during reinforcement processing and provide insight on the role of neuro-computational processes underlying reinforcement learning/decision-making in adolescents.

Blue wavelength light has been used successfully as a treatment method for certain mood disorders, but, the underlying mechanisms behind the mood enhancing effects of light remain poorly understood. We investigated the effects of a single dose of 30 min of blue wavelength light (n = 17) vs. amber wavelength light (n = 12) exposure in a sample of healthy adults on subsequent resting-state functional and directed connectivity, and associations with changes in state affect. Individuals who received blue vs. amber wavelength light showed greater positive connectivity between the right amygdala and a region within the left dorsolateral prefrontal cortex (DLPFC). In addition, using granger causality, the findings showed that individuals who received blue wavelength light displayed greater bidirectional information flow between these two regions relative to amber light. Furthermore, the strength of amygdala-DLPFC functional connectivity was associated with greater decreases in negative mood for the blue, but not the amber light condition. Blue light exposure may positively influence mood by modulating greater information flow between the amygdala and the DLPFC, which may result in greater engagement of cognitive control strategies that are needed to perceive and regulate arousal and mood.

Sleep and circadian rhythms are among the most powerful but least understood contributors to cognitive performance and brain health. Here we capitalize on the circadian resetting effect of blue-wavelength light to phase shift the sleep patterns of adult patients (aged 18–48 years) recovering from mild traumatic brain injury (mTBI), with the aim of facilitating recovery of brain structure, connectivity, and cognitive performance. During a randomized, double-blind, placebo-controlled trial of 32 adults with a recent mTBI, we compared 6-weeks of daily 30-min pulses of blue light (peak λ = 469 nm) each morning versus amber placebo light (peak λ = 578 nm) on neurocognitive and neuroimaging outcomes, including gray matter volume (GMV), resting-state functional connectivity, directed connectivity using Granger causality, and white matter integrity using diffusion tensor imaging (DTI). Relative to placebo, morning blue light led to phase-advanced sleep timing, reduced daytime sleepiness, and improved executive functioning, and was associated with increased volume of the posterior thalamus (i.e., pulvinar), greater thalamo-cortical functional connectivity, and increased axonal integrity of these pathways. These findings provide insight into the contributions of the circadian and sleep systems in brain repair and lay the groundwork for interventions targeting the retinohypothalamic system to facilitate injury recovery.

The biggest nationwide COVID-19 pandemic lockdown worldwide was enforced in India for an initial period of 21-days. Emerging evidence suggests that pandemic situations and associated lockdowns have an adverse impact on sleep and mental health. However, prediction of sleep health from sociodemographic characteristics and the public's worry during the initial stages of the COVID-19 pandemic has not been extensively explored so far. It's also unclear whether sleep outcomes mediate the association between worry and mental health during pandemic situations. A web-survey (N = 391) on sociodemographic characteristics, COVID-19 related worry, sleep health (insomnia and daytime sleepiness), and depression was conducted during the initial 21-days of the COVID-19 stringent lockdown in India. Multiple regression analyses showed that variables, including sex, age, income level, and worry score, contributed to the significant regression equation for insomnia but not for daytime sleepiness. Specifically, the female, younger, lower income, and highly worried populations contributed significantly more than the male, elderly, higher income, and less worried populations, respectively, to the prediction of insomnia. Mediation analyses showed that insomnia, but not daytime sleepiness, fully mediated the relationship between worry score and severity of depressive symptoms. We provide evidence that the female, younger, lower income, and worried populations may be at higher risk for insomnia during pandemic situations. Current evidence gives hope that improving sleep may reduce depressive symptoms during a pandemic situation. This underscores the importance of the implementation of effective public health policies in conjunction with strategical responses to the COVID-19 pandemic.

Abstract Chronic sleep restriction is a common experience; and while it has negative physiological effects, little is known about how it affects human behavior. To date, no study has investigated whether chronic sleep restriction can influence implicit attitudes (e.g., towards a race). Here, in a randomized, counterbalanced crossover design, we subjected participants to 3 weeks of chronic sleep restriction in the lab (i.e., 3 weekly cycles of 5 nights of 4 hours of sleep per night followed by 2 nights of 8 hours of sleep) and found evidence for an increased negative implicit bias towards Arab Muslims. No indicators of an implicit bias were found in these same individuals when they were rested (during a counterbalanced 3-week period of 8 hours time in bed per night). These findings suggest that chronic sleep restriction may “unmask” implicit racial or ethnic biases that are otherwise inhibited when in a rested state. Because chronic sleep restriction is prevalent among many occupations that routinely interact with ethnic minorities in potentially high-conflict situations (e.g., police officers), it is critical to consider the role that restricted sleep may play in exacerbating negative implicit attitudes and their potential for provoking unintentional and potentially harmful behavioral consequences.

Suicidal ideation (SUI) can occur in the absence of concomitant psychiatric diagnoses, and even normal levels can be problematic among individuals experiencing excess stress or lack of social support. The objective of this study was to investigate the neuroanatomical basis of SUI in non-clinical human populations who are within the normal limits of SUI, after accounting for elevated stress and perceived lack of social support. Neuroanatomical data were collected from 55 healthy individuals (mean age 30.9 ± 8.1 years, 27 females) whose depression severity levels were below the Diagnostic and Statistical Manual of Mental Disorders criteria. Measures of SUI, aggression, stress, non-support, and treatment rejection were collected from the treatment-consideration scales (TCS) of the Personality Assessment Inventory (PAI). Correlations between standardized SUI scores and three brain morphometry measures, including vertex wise cortical thickness (CT), cortical surface area (CSA), and cortical volume (CV), were estimated for each participant, controlling for age, sex, intracranial volume, and the remaining TCS measures. We observed a significant negative association between scores on SUI and both CSA and CV (cluster-forming threshold of p < 0.005, clusterwise threshold of p < 0.05, FDR corrected for multiple comparisons) within the left rostral middle frontal gyrus. Our findings suggest that greater CSA and CV within the dorsolateral prefrontal cortex are associated with reduced SUI in a non-clinical population with mild levels of stress and perceived lack of social support. Because the dorsolateral prefrontal cortex has been broadly linked to cognitive reappraisal, self-critical thoughts, and emotional regulation, greater CSA and CV within these regions may lead to better mental health by protecting healthy individuals from engaging in SUI during periods of stress and perceived insufficient social support. As our data consisted of only healthy individuals with non-clinical levels of SUI, further investigation will be necessary to explore the neural basis of SUI in populations who may be at greater risk of future suicidal behavior

Mild traumatic brain injury (mTBI) remains the most commonly reported head injury in the United States, and is associated with a wide range of post-concussive symptoms including physical, cognitive and affective impairments. Elevated aggression has been documented in mTBI; however, the neural mechanisms associated with aggression at the chronic stage of recovery remain poorly understood. In the present study, we investigated the association between white matter integrity and aggression in mTBI using diffusion tensor imaging (DTI). Twenty-six age-matched adults participated in the study, including 16 healthy controls (HCs) and 10 individuals in the chronic stage of recovery (either 6-months or 12 months post-mTBI). Psychological measures of aggression included the Buss-Perry Aggression Questionnaire and the Personality Assessment Inventory (PAI). Axonal pathways implicated in affective processing were studied, including the corpus callosum, anterior thalamic radiation, cingulum and uncinate fasciculus, and measures of white matter integrity included fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD) and axial diffusivity (AD). We found that adults with mTBI in the chronic stage of recovery had higher levels aggression. Individuals with mTBI also had greater RD in the corpus callosum compared to HCs, indicating reduced fiber integrity. Furthermore, we observed a significant association between reduced white matter integrity in the corpus callosum and greater aggression. Our findings provide additional evidence for underlying neuroanatomical mechanisms of aggression, although future research will be necessary to characterize the specific relationship between aggression and the white matter pathways we identified.

Background: Mild traumatic brain injuries (mTBIs) are a significant social, sport, and military health issue. In spite of advances in the clinical management of these injuries, the underlying pathophysiology is not well-understood. There is a critical need to advance objective biomarkers, allowing the identification and tracking of the long-term evolution of changes resulting from mTBI. Diffusion-weighted imaging (DWI) allows for the assessment of white-matter properties in the brain and shows promise as a suitable biomarker of mTBI pathophysiology.Methods: 34 individuals within a year of an mTBI (age: 24.4 ± 7.4) and 18 individuals with no history of mTBI (age: 23.2 ± 3.4) participated in this study. Participants completed self-report measures related to functional outcomes, psychological health, post-injury symptoms, and sleep, and underwent a neuroimaging session that included DWI. Whole-brain white matter was skeletonized using tract-based spatial statistics (TBSS) and compared between groups as well as correlated within-group with the self-report measures.Results: There were no statistically significant anatomical differences between the two groups. After controlling for time since injury, fractional anisotropy (FA) demonstrated a negative correlation with sleep quality scores (higher FA was associated with better sleep quality) and increasing depressive symptoms in the mTBI participants. Conversely, mean (MD) and radial diffusivity (RD) demonstrated positive correlations with sleep quality scores (higher RD was associated with worse sleep quality) and increasing depressive symptoms. These correlations were observed bilaterally in the internal capsule (anterior and posterior limbs), corona radiata (anterior and superior), fornix, and superior fronto-occipital fasciculi.Conclusion: The results of this study indicate that the clinical presentation of mTBI, particularly with respect to depression and sleep, is associated with reduced white-matter integrity in multiple areas of the brain, even after controlling for time since injury. These areas are generally associated not only with sleep and emotion regulation but also cognition. Consequently, the onset of depression and sleep dysfunction as well as cognitive impairments following mTBI may be closely related to each other and to white-matter integrity throughout the brain.

Despite extensive research in the field of aging neuroscience, it still remains unclear whether age related cortical changes can be detected in different functional networks of younger adults and whether these networks respond identically to healthy aging. We collected high-resolution brain anatomical data from 56 young healthy adults (mean age = 30.8 ± 8.1 years, 29 males). We performed whole brain parcellation into seven functional networks, including visual, somatomotor, dorsal attention, ventral attention, limbic, frontoparietal and default mode networks. We estimated intracranial volume (ICV) and averaged cortical thickness (CT), cortical surface area (CSA) and cortical volume (CV) over each hemisphere as well as for each network. Averaged cortical measures over each hemisphere, especially CT and CV, were significantly lower in older individuals compared to younger ones (one-way ANOVA, p < 0.05, corrected for multiple comparisons). There were negative correlations between age and averaged CT and CV over each hemisphere (p < 0.05, corrected for multiple comparisons) as well as between age and ICV (p = 0.05). Network level analysis showed that age was negatively correlated with CT for all functional networks (p < 0.05, corrected for multiple comparisons), apart from the limbic network. While age was unrelated to CSA, it was negatively correlated with CV across several functional networks (p < 0.05, corrected for multiple comparisons). We also showed positive associations between CV and CT and between CV and CSA for all networks (p < 0.05, corrected for multiple comparisons). We interpret the lack of association between age and CT of the limbic network as evidence that the limbic system may be particularly resistant to age-related declines during this period of life, whereas the significant age-related declines in averaged CT over each hemisphere as well as in all other six networks suggests that CT may serve as a reliable biomarker to capture the effect of normal aging. Due to the simultaneous dependence of CV on CT and CSA, CV was unable to identify such effects of normal aging consistently for the other six networks, but there were negative associations observed between age and averaged CV over each hemisphere as well as between age and ICV. Our findings suggest that the identification of early cortical changes within various functional networks during normal aging might be useful for predicting the effect of aging on the efficiency of functional performance even during early adulthood.

Mild traumatic brain injury (mTBI) is a common and often inconspicuous wound that is frequently associated with chronic low-grade symptoms and cognitive dysfunction. Previous evidence suggests that daily blue wavelength light therapy may be effective at reducing fatigue and improving sleep in patients recovering from mTBI. However, the effects of light therapy on recovering brain structure remain unexplored. In this study, we analyzed white matter diffusion properties, including generalized fractional anisotropy, and the quantity of water diffusion in isotropic (i.e., isotropic diffusion) and anisotropic fashion (i.e., quantitative anisotropy, QA) for fibers crossing 11 brain areas known to be significantly affected following mTBI. Specifically, we investigated how 6 weeks of daily morning blue light exposure therapy (compared to an amber-light placebo condition) impacted changes in white matter diffusion in individuals with mTBI. We observed a significant impact of the blue light treatment (relative to the placebo) on the amount of water diffusion (QA) for multiple brain areas, including the corpus callosum, anterior corona radiata, and thalamus. Moreover, many of these changes were associated with improvements in sleep latency and delayed memory. These findings suggest that blue wavelength light exposure may serve as one of the potential non-pharmacological treatments for facilitating structural and functional recovery following mTBI; they also support the use of QA as a reliable neuro-biomarker for mTBI therapies.

Brain areas within the motor system interact directly or indirectly during motor-imagery and motor-execution tasks. These interactions and their functionality can change following stroke and recovery. How brain network interactions reorganize and recover their functionality during recovery and treatment following stroke are not well understood. To contribute to answering these questions, we recorded blood oxygenation-level dependent (BOLD) functional magnetic resonance imaging (fMRI) signals from 10 stroke survivors and evaluated dynamical causal modeling (DCM)-based effective connectivity among three motor areas: primary motor cortex (M1), pre-motor cortex (PMC) and supplementary motor area (SMA), during motor-imagery and motor-execution tasks. We compared the connectivity between affected and unaffected hemispheres before and after mental practice and combined mental practice and physical therapy as treatments. The treatment (intervention) period varied in length between 14 to 51 days but all patients received the same dose of 60 h of treatment. Using Bayesian model selection (BMS) approach in the DCM approach, we found that, after intervention, the same network dominated during motor-imagery and motor-execution tasks but modulatory parameters suggested a suppressive influence of SM A on M1 during the motor-imagery task whereas the influence of SM A on M1 was unrestricted during the motor-execution task. We found that the intervention caused a reorganization of the network during both tasks for unaffected as well as for the affected hemisphere. Using Bayesian model averaging (BMA) approach, we found that the intervention improved the regional connectivity among the motor areas during both the tasks. The connectivity between PMC and M1 was stronger in motor-imagery tasks whereas the connectivity from PMC to M1, SM A to M1 dominated in motor-execution tasks. There was significant behavioral improvement (p = 0.001) in sensation and motor movements because of the intervention as reflected by behavioral Fugl-Meyer (FMA) measures, which were significantly correlated (p = 0.05) with a subset of connectivity. These findings suggest that PMC and M1 play a crucial role during motor-imagery as well as during motor-execution task. In addition, M1 causes more exchange of causal information among motor areas during a motor-execution task than during a motor-imagery task due to its interaction with SM A. This study expands our understanding of motor network involved during two different tasks, which are commonly used during rehabilitation following stroke. A clear understanding of the effective connectivity networks leads to a better treatment in helping stroke survivors regain motor ability.

Acute exposure to light within the blue wavelengths has been shown to enhance alertness and vigilance, and lead to improved speed on reaction time tasks, possibly due to activation of the noradrenergic system. It remains unclear, however, whether the effects of blue light extend beyond simple alertness processes to also enhance other aspects of cognition, such as memory performance. The aim of this study was to investigate the effects of a thirty minute pulse of blue light versus placebo (amber light) exposure in healthy normally rested individuals in the morning during verbal memory consolidation (i.e., 1.5 hours after memory acquisition) using an abbreviated version of the California Verbal Learning Test (CVLT-II). At delayed recall, individuals who received blue light (n = 12) during the consolidation period showed significantly better long-delay verbal recall than individuals who received amber light exposure (n = 18), while controlling for the effects of general intelligence, depressive symptoms and habitual wake time. These findings extend previous work demonstrating the effect of blue light on brain activation and alertness to further demonstrate its effectiveness at facilitating better memory consolidation and subsequent retention of verbal material. Although preliminary, these findings point to a potential application of blue wavelength light to optimize memory performance in healthy populations. It remains to be determined whether blue light exposure may also enhance performance in clinical populations with memory deficits.

The brain remains electrically and metabolically active during resting conditions. The low-frequency oscillations (LFO) of the blood oxygen level-dependent (BOLD) signal of functional magnetic resonance imaging (fMRI) coherent across distributed brain regions are known to exhibit features of this activity. However, these intrinsic oscillations may undergo dynamic changes in time scales of seconds to minutes during resting conditions. Here, using wavelet-transform based time-frequency analysis techniques, we investigated the dynamic nature of default-mode networks from intrinsic BOLD signals recorded from participants maintaining visual fixation during resting conditions. We focused on the default-mode network consisting of the posterior cingulate cortex (PCC), the medial prefrontal cortex (mPFC), left middle temporal cortex (LMTC) and left angular gyrus (LAG). The analysis of the spectral power and causal flow patterns revealed that the intrinsic LFO undergo significant dynamic changes over time. Dividing the frequency interval 0 to 0.25 Hz of LFO into four intervals slow-5 (0.01-0.027 Hz), slow-4 (0.027-0.073 Hz), slow-3 (0.073-0.198 Hz) and slow-2 (0.198-0.25 Hz), we further observed significant positive linear relationships of slow-4 in-out flow of network activity with slow-5 node activity, and slow-3 in-out flow of network activity with slow-4 node activity. The network activity associated with respiratory related frequency (slow-2) was found to have no relationship with the node activity in any of the frequency intervals. We found that the net causal flow towards a node in slow-3 band was correlated with the number of fibers, obtained from diffusion tensor imaging (DTI) data, from the other nodes connecting to that node. These findings imply that so-called resting state is not 'entirely' at rest, the higher frequency network activity flow can predict the lower frequency node activity, and the network activity flow can reflect underlying structural connectivity.