Brain-age prediction: Systematic evaluation of site effects, and sample age range and size.

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model. Here we expand this work to develop, empirically validate, and disseminate a pre-trained brain-age model to cover most of the human lifespan. To achieve this, we selected the best-performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5-90 years; 53.59% female). The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8-80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9-25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age-bins (5-40 and 40-90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain-age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open-science, web-based platform for individualized neuroimaging metrics.

Altered task-related decoupling of the rostral anterior cingulate cortex in depression.

Dysfunctional activity of the rostral anterior cingulate cortex (rACC) - an extensively connected hub region of the default mode network - has been broadly linked to cognitive and affective impairments in depression. However, the nature of aberrant task-related rACC suppression in depression is incompletely understood. In this study, we sought to characterize functional connectivity of rACC activity suppression ('deactivation') - an essential feature of rACC function - during external task engagement in depression. Specifically, we aimed to explore neural patterns of functional decoupling and coupling with the rACC during its task-driven suppression. We enrolled 81 15- to 25-year-old young people with moderate-to-severe major depressive disorder (MDD) before they commenced a 12-week clinical trial that assessed the effectiveness of cognitive behavioral therapy plus either fluoxetine or placebo. Ninety-four matched healthy controls were also recruited. Participants completed a functional magnetic resonance imaging face matching task known to elicit rACC suppression. To identify brain regions associated with the rACC during its task-driven suppression, we employed a seed-based functional connectivity analysis. We found MDD participants, compared to controls, showed significantly reduced 'decoupling' of the rACC with extended task-specific regions during task performance. Specifically, less decoupling was observed in the occipital and fusiform gyrus, dorsal ACC, medial prefrontal cortex, cuneus, amygdala, thalamus, and hippocampus. Notably, impaired decoupling was apparent in participants who did not remit to treatment, but not treatment remitters. Further, we found MDD participants showed significant increased coupling with the anterior insula cortex during task engagement. Our findings indicate that aberrant task-related rACC suppression is associated with disruptions in adaptive neural communication and dynamic switching between internal and external cognitive modes that may underpin maladaptive cognitions and biased emotional processing in depression.

Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures.

Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects.

Major depressive disorder associated alterations in the effective connectivity of the face processing network: a systematic review.

Major depressive disorder (MDD) is marked by altered processing of emotional stimuli, including facial expressions. Recent neuroimaging research has attempted to investigate how these stimuli alter the directional interactions between brain regions in those with MDD; however, methodological heterogeneity has made identifying consistent effects difficult. To address this, we systematically examined studies investigating MDD-associated differences present in effective connectivity during the processing of emotional facial expressions. We searched five databases: PsycINFO, EMBASE, PubMed, Scopus, and Web of Science, using a preregistered protocol (registration number: CRD42021271586). Of the 510 unique studies screened, 17 met our inclusion criteria. These studies identified that compared with healthy controls, participants with MDD demonstrated (1) reduced connectivity from the dorsolateral prefrontal cortex to the amygdala during the processing of negatively valenced expressions, and (2) increased inhibitory connectivity from the ventromedial prefrontal cortex to amygdala during the processing of happy facial expressions. Most studies investigating the amygdala and anterior cingulate cortex noted differences in their connectivity; however, the precise nature of these differences was inconsistent between studies. As such, commonalities observed across neuroimaging modalities warrant careful investigation to determine the specificity of these effects to particular subregions and emotional expressions. Future research examining longitudinal connectivity changes associated with treatment response may provide important insights into mechanisms underpinning therapeutic interventions, thus enabling more targeted treatment strategies.

Normative Modeling of Brain Morphometry Across the Lifespan Using CentileBrain: Algorithm Benchmarking and Model Optimization.

We present an empirically benchmarked framework for sex-specific normative modeling of brain morphometry that can inform about the biological and behavioral significance of deviations from typical age-related neuroanatomical changes and support future study designs. This framework was developed using regional morphometric data from 37,407 healthy individuals (53% female; aged 3-90 years) following a comparative evaluation of eight algorithms and multiple covariate combinations pertaining to image acquisition and quality, parcellation software versions, global neuroimaging measures, and longitudinal stability. The Multivariate Factorial Polynomial Regression (MFPR) emerged as the preferred algorithm optimized using nonlinear polynomials for age and linear effects of global measures as covariates. The MFPR models showed excellent accuracy across the lifespan and within distinct age-bins, and longitudinal stability over a 2-year period. The performance of all MFPR models plateaued at sample sizes exceeding 3,000 study participants. The model and scripts described here are freely available through CentileBrain (https://centilebrain.org/).

A brain model of altered self-appraisal in social anxiety disorder.

The brain's default mode network has a central role in the processing of information concerning oneself. Dysfunction in this self-referential processing represents a key component of multiple mental health conditions, particularly social anxiety disorder (SAD). This case-control study aimed to clarify alterations to network dynamics present during self-appraisal in SAD participants. A total of 38 adolescents and young adults with SAD and 72 healthy control participants underwent a self-referential processing fMRI task. The task involved two primary conditions of interest: direct self-appraisal (thinking about oneself) and reflected self-appraisal (thinking about how others might think about oneself). Dynamic causal modeling and parametric empirical Bayes were then used to explore differences in the effective connectivity of the default mode network between groups. We observed connectivity differences between SAD and healthy control participants in the reflected self-appraisal but not the direct self-appraisal condition. Specifically, SAD participants exhibited greater excitatory connectivity from the posterior cingulate cortex (PCC) to medial prefrontal cortex (MPFC) and greater inhibitory connectivity from the inferior parietal lobule (IPL) to MPFC. In contrast, SAD participants exhibited reduced intrinsic connectivity in the absence of task modulation. This was illustrated by reduced excitatory connectivity from the PCC to MPFC and reduced inhibitory connectivity from the IPL to MPFC. As such, participants with SAD showed changes to afferent connections to the MPFC which occurred during both reflected self-appraisal as well as intrinsically. The presence of connectivity differences in reflected and not direct self-appraisal is consistent with the characteristic fear of negative social evaluation that is experienced by people with SAD.

The Study of Ketamine for Youth Depression (SKY-D): study protocol for a randomised controlled trial of low-dose ketamine for young people with major depressive disorder.

BACKGROUND: Existing treatments for young people with severe depression have limited effectiveness. The aim of the Study of Ketamine for Youth Depression (SKY-D) trial is to determine whether a 4-week course of low-dose subcutaneous ketamine is an effective adjunct to treatment-as-usual in young people with major depressive disorder (MDD). METHODS: SKY-D is a double-masked, randomised controlled trial funded by the Australian Government's National Health and Medical Research Council (NHMRC). Participants aged between 16 and 25 years (inclusive) with moderate-to-severe MDD will be randomised to receive either low-dose ketamine (intervention) or midazolam (active control) via subcutaneous injection once per week for 4 weeks. The primary outcome is change in depressive symptoms on the Montgomery-Åsberg Depression Rating Scale (MADRS) after 4 weeks of treatment. Further follow-up assessment will occur at 8 and 26 weeks from treatment commencement to determine whether treatment effects are sustained and to investigate safety outcomes. DISCUSSION: Results from this trial will be important in determining whether low-dose subcutaneous ketamine is an effective treatment for young people with moderate-to-severe MDD. This will be the largest randomised trial to investigate the effects of ketamine to treat depression in young people. TRIAL REGISTRATION: Australian and New Zealand Clinical Trials Registry ID: ACTRN12619000683134. Registered on May 7, 2019. https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=377513 .

Kupffer cells prevent pancreatic ductal adenocarcinoma metastasis to the liver in mice.

Although macrophages contribute to cancer cell dissemination, immune evasion, and metastatic outgrowth, they have also been reported to coordinate tumor-specific immune responses. We therefore hypothesized that macrophage polarization could be modulated therapeutically to prevent metastasis. Here, we show that macrophages respond to ß-glucan (odetiglucan) treatment by inhibiting liver metastasis. ß-glucan activated liver-resident macrophages (Kupffer cells), suppressed cancer cell proliferation, and invoked productive T cell-mediated responses against liver metastasis in pancreatic cancer mouse models. Although excluded from metastatic lesions, Kupffer cells were critical for the anti-metastatic activity of ß-glucan, which also required T cells. Furthermore, ß-glucan drove T cell activation and macrophage re-polarization in liver metastases in mice and humans and sensitized metastatic lesions to anti-PD1 therapy. These findings demonstrate the significance of macrophage function in metastasis and identify Kupffer cells as a potential therapeutic target against pancreatic cancer metastasis to the liver.

Revisiting deficits in threat and safety appraisal in obsessive-compulsive disorder.

Current behavioural treatment of obsessive-compulsive disorder (OCD) is informed by fear conditioning and involves iteratively re-evaluating previously threatening stimuli as safe. However, there is limited research investigating the neurobiological response to conditioning and reversal of threatening stimuli in individuals with OCD. A clinical sample of individuals with OCD (N = 45) and matched healthy controls (N = 45) underwent functional magnetic resonance imaging. While in the scanner, participants completed a well-validated fear reversal task and a resting-state scan. We found no evidence for group differences in task-evoked brain activation or functional connectivity in OCD. Multivariate analyses encompassing all participants in the clinical and control groups suggested that subjective appraisal of threatening and safe stimuli were associated with a larger difference in brain activity than the contribution of OCD symptoms. In particular, we observed a brain-behaviour continuum whereby heightened affective appraisal was related to increased bilateral insula activation during the task (r = 0.39, pFWE = .001). These findings suggest that changes in conditioned threat-related processes may not be a core neurobiological feature of OCD and encourage further research on the role of subjective experience in fear conditioning.

Regional, circuit and network heterogeneity of brain abnormalities in psychiatric disorders.

The substantial individual heterogeneity that characterizes people with mental illness is often ignored by classical case-control research, which relies on group mean comparisons. Here we present a comprehensive, multiscale characterization of the heterogeneity of gray matter volume (GMV) differences in 1,294 cases diagnosed with one of six conditions (attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, depression, obsessive-compulsive disorder and schizophrenia) and 1,465 matched controls. Normative models indicated that person-specific deviations from population expectations for regional GMV were highly heterogeneous, affecting the same area in <7% of people with the same diagnosis. However, these deviations were embedded within common functional circuits and networks in up to 56% of cases. The salience-ventral attention system was implicated transdiagnostically, with other systems selectively involved in depression, bipolar disorder, schizophrenia and attention-deficit/hyperactivity disorder. Phenotypic differences between cases assigned the same diagnosis may thus arise from the heterogeneous localization of specific regional deviations, whereas phenotypic similarities may be attributable to the dysfunction of common functional circuits and networks.

Frontoamygdalar Effective Connectivity in Youth Depression and Treatment Response.

BACKGROUND: Emotion regulation deficits are characteristic of youth depression and are underpinned by altered frontoamygdalar function. However, the causal dynamics of frontoamygdalar pathways in depression and how these dynamics relate to treatment prognosis remain unexplored. This study aimed to assess frontoamygdalar effective connectivity during cognitive reappraisal in youths with depression and to test whether pathway dynamics are predictive of individual response to combined cognitive behavioral therapy plus treatment with fluoxetine or placebo. METHODS: One hundred seven young people with moderate to severe depression and 94 healthy control participants completed a functional magnetic resonance imaging cognitive reappraisal task. After the task, 87 participants with depression were randomized and received 12 weeks of cognitive behavioral therapy plus either fluoxetine or placebo. Dynamic causal modeling was used to map frontoamygdalar effective connectivity during reappraisal and to assess the predictive capacity of baseline frontoamygdalar effective connectivity on depression diagnosis and posttreatment depression remission. RESULTS: Young people with depression showed weaker inhibitory modulation of ventrolateral prefrontal cortex to amygdala connectivity during reappraisal (0.29 Hz, posterior probability = 1.00). Leave-one-out cross-validation demonstrated that this effect was sufficiently large to predict individual diagnostic status (r = 0.20, p = .003). Posttreatment depression remission was associated with weaker excitatory ventromedial prefrontal cortex to amygdala connectivity (-0.56 Hz, posterior probability = 1.00) during reappraisal at baseline, though this effect did not predict individual remission status (r = -0.02, p = .561). CONCLUSIONS: Frontoamygdalar effective connectivity shows promise in identifying youth depression diagnosis, and circuits responsible for negative affect regulation are implicated in responsiveness to first-line depression treatments.

Differential engagement of the posterior cingulate cortex during cognitive restructuring of negative self- and social beliefs.

Negative self-beliefs are a core feature of psychopathology, encompassing both negative appraisals about oneself directly (i.e. self-judgment) and negative inferences of how the self is appraised by others (i.e. social judgment). Challenging maladaptive self-beliefs via cognitive restructuring is a core treatment mechanism of gold-standard psychotherapies. However, the neural mechanisms underlying the restructuring of these two kinds of negative self-beliefs are poorly understood. Eighty-six healthy participants cognitively restructured self-judgment and social-judgment negative self-belief statements during 7 Tesla functional magnetic resonance imaging scanning. Cognitive restructuring broadly elicited activation in the core default mode network (DMN), salience and frontoparietal control regions. Restructuring self-judgment relative to social-judgment beliefs was associated with comparatively higher activation in the ventral posterior cingulate cortex (PCC)/retrosplenial cortex, while challenging social-judgment statements was associated with higher activation in the dorsal PCC/precuneus. While both regions showed increased functional connectivity with the supplementary and pre-supplementary motor areas during restructuring, the dorsal PCC displayed greater task-dependent connectivity with distributed regions involved in salience, attention and social cognition. Our findings indicate distinct patterns of PCC engagement contingent upon self- and social domains, highlighting a specialized role of the dorsal PCC in supporting neural interactions between the DMN and frontoparietal/salience networks during cognitive restructuring.

Altered interaction of physiological activity and behavior affects risky decision-making in ADHD.

Background: Adult attention-deficit/hyperactivity disorder (ADHD) is often associated with risky decision-making behavior. However, current research studies are often limited by the ability to adequately reflect daily behavior in a laboratory setting. Over the lifespan impairments in cognitive functions appear to improve, whereas affective functions become more severe. We assume that risk behavior in ADHD arises predominantly from deficits in affective processes. This study will therefore aim to investigate whether a dysfunction in affective pathways causes an abnormal risky decision-making (DM) behavior in adult ADHD. Methods: Twenty-eight participants with ADHD and twenty-eight healthy controls completed a battery of questionnaires regarding clinical symptoms, self-assessment of behavior and emotional competence. Furthermore, skin conductance responses were measured during the performance in a modified version of the Balloon Analogue Risk Task. A linear mixed-effects model analysis was used to analyze emotional arousal prior to a decision and after feedback display. Results: Results showed higher emotional arousal in ADHD participants before decision-making (ß = -0.12, SE = 0.05, t = -2.63, p < 0.001) and after feedback display (ß = -0.14, SE = 0.05, t = -2.66, p = 0.008). Although risky behavior was greater in HC than in ADHD, we found a significant interaction effect of group and anticipatory skin conductance responses regarding the response behavior (ß = 107.17, SE = 41.91, t = 2.56, p = 0.011). Post hoc analyses revealed a positive correlation between anticipatory skin conductance responses and reaction time in HC, whereas this correlation was negative in ADHD. Self-assessment results were in line with the objective measurements. Conclusion: We found altered changes in physiological activity during a risky decision-making task. The results confirm the assumption of an aberrant relationship between bodily response and risky behavior in adult ADHD. However, further research is needed with respect to age and gender when considering physiological activities.

Use of steerable sheaths for complex aortic procedures.

OBJECTIVES: To demonstrate the ease with which steerable sheaths, designed for cardiac electrophysiological applications, can be used to aid endovascular treatment of a wide range of non-cardiac vascular disease and to assist with target vessel cannulation in branched and fenestrated aortic grafts. METHODS: A retrospective medical chart review was carried out to identify cases from a single vascular surgery unit (2019-2022) where the HeartSpan Steerable Sheath (HSS) (Merit Medical, South Jordan, UT, USA) was utilised to enable endovascular management of complex vascular pathology. A case presentation of branch graft insertion performed entirely via distal access is described and used to help identify pertinent sheath characteristics and technical considerations, and to illustrate the advantages and disadvantages of the design for modified use in target vessel cannulation. RESULTS: The HSS was used in the endovascular treatment of different vascular pathologies in 15 patients (23 target vessels) where access to the vessels using standard catheters and approaches was not possible. Cannulation and subsequent stenting were successful for 21 of the 23 target vessels in total. Of these cases, the HSS was used as an adjunct for deployment of fenestrated endovascular graft systems when conventional techniques for canulation of target vessels had failed on five occasions. On another four occasions, the HSS enabled full deployment of the entire Zenith® t-Branch™ Thoracoabdominal Endovascular Graft system from an exclusively femoral approach. An additional three cases involved use of the HSS for superior mesenteric artery cannulation in patients with mesenteric ischaemia. The device was also used once in each of the following cases: contralateral common iliac cannulation, cannulation of contralateral internal iliac artery for coil embolisation, and access of a contralateral iliac branched device. There were no stent dislocations and all aortic branches that were patent at the completion of each case remained so 1-year post procedure. CONCLUSION: Steerable sheaths designed for cardiac electrophysical applications, like the HSS Introducer, can be successfully utilised for cannulation of challenging target vessels in a wide range of aortic endovascular procedures. This modified approach may salvage cases that would otherwise be considered inoperable in regions of the world where steerable sheaths designed for aortic use are not readily available.

Concurrent validity and reliability of suicide risk assessment instruments: A meta-analysis of 20 instruments across 27 international cohorts.

OBJECTIVE: A major limitation of current suicide research is the lack of power to identify robust correlates of suicidal thoughts or behavior. Variation in suicide risk assessment instruments used across cohorts may represent a limitation to pooling data in international consortia. METHOD: Here, we examine this issue through two approaches: (a) an extensive literature search on the reliability and concurrent validity of the most commonly used instruments and (b) by pooling data (N ∼ 6,000 participants) from cohorts from the Enhancing NeuroImaging Genetics Through Meta-Analysis (ENIGMA) Major Depressive Disorder and ENIGMA-Suicidal Thoughts and Behaviour working groups, to assess the concurrent validity of instruments currently used for assessing suicidal thoughts or behavior. RESULTS: We observed moderate-to-high correlations between measures, consistent with the wide range (κ range: 0.15-0.97; r range: 0.21-0.94) reported in the literature. Two common multi-item instruments, the Columbia Suicide Severity Rating Scale and the Beck Scale for Suicidal Ideation were highly correlated with each other (r = 0.83). Sensitivity analyses identified sources of heterogeneity such as the time frame of the instrument and whether it relies on self-report or a clinical interview. Finally, construct-specific analyses suggest that suicide ideation items from common psychiatric questionnaires are most concordant with the suicide ideation construct of multi-item instruments. CONCLUSIONS: Our findings suggest that multi-item instruments provide valuable information on different aspects of suicidal thoughts or behavior but share a modest core factor with single suicidal ideation items. Retrospective, multisite collaborations including distinct instruments should be feasible provided they harmonize across instruments or focus on specific constructs of suicidality. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Individuals with problem gambling and obsessive-compulsive disorder learn through distinct reinforcement mechanisms.

Obsessive-compulsive disorder (OCD) and pathological gambling (PG) are accompanied by deficits in behavioural flexibility. In reinforcement learning, this inflexibility can reflect asymmetric learning from outcomes above and below expectations. In alternative frameworks, it reflects perseveration independent of learning. Here, we examine evidence for asymmetric reward-learning in OCD and PG by leveraging model-based functional magnetic resonance imaging (fMRI). Compared with healthy controls (HC), OCD patients exhibited a lower learning rate for worse-than-expected outcomes, which was associated with the attenuated encoding of negative reward prediction errors in the dorsomedial prefrontal cortex and the dorsal striatum. PG patients showed higher and lower learning rates for better- and worse-than-expected outcomes, respectively, accompanied by higher encoding of positive reward prediction errors in the anterior insula than HC. Perseveration did not differ considerably between the patient groups and HC. These findings elucidate the neural computations of reward-learning that are altered in OCD and PG, providing a potential account of behavioural inflexibility in those mental disorders.

Subcortical contributions to salience network functioning during negative emotional processing.

Core regions of the salience network (SN), including the anterior insula (aINS) and dorsal anterior cingulate cortex (dACC), coordinate rapid adaptive changes in attentional and autonomic processes in response to negative emotional events. In doing so, the SN incorporates bottom-up signals from subcortical brain regions, such as the amygdala and periaqueductal gray (PAG). However, the precise influence of these subcortical regions is not well understood. Using ultra-high field 7-Tesla functional magnetic resonance imaging, this study investigated the bottom-up interactions of the amygdala and PAG with the SN during negative emotional salience processing. Thirty-seven healthy participants completed an emotional oddball paradigm designed to elicit a salient negative emotional response via the presentation of random, task-irrelevant negative emotional images. Negative emotional processing was associated with prominent activation in the SN, spanning the amygdala, PAG, aINS, and dACC. Consistent with previous research, analysis using dynamic causal modelling revealed an excitatory influence from the amygdala to the aINS, dACC, and PAG. In contrast, the PAG showed an inhibitory influence on amygdala, aINS and dACC activity. Our findings suggest that the amygdala may amplify the processing of negative emotional stimuli in the SN to enable upstream access to attentional resources. In comparison, the inhibitory influence of the PAG possibly reflects its involvement in modulating sympathetic-parasympathetic autonomic arousal mediated by the SN. This PAG-mediated effect may be driven by amygdala input and facilitate bottom-up processing of negative emotional stimuli. Overall, our results show that the amygdala and PAG modulate divergent functions of the SN during negative emotional processing.

Functional connectivity signatures of major depressive disorder: machine learning analysis of two multicenter neuroimaging studies.

The promise of machine learning has fueled the hope for developing diagnostic tools for psychiatry. Initial studies showed high accuracy for the identification of major depressive disorder (MDD) with resting-state connectivity, but progress has been hampered by the absence of large datasets. Here we used regular machine learning and advanced deep learning algorithms to differentiate patients with MDD from healthy controls and identify neurophysiological signatures of depression in two of the largest resting-state datasets for MDD. We obtained resting-state functional magnetic resonance imaging data from the REST-meta-MDD (N = 2338) and PsyMRI (N = 1039) consortia. Classification of functional connectivity matrices was done using support vector machines (SVM) and graph convolutional neural networks (GCN), and performance was evaluated using 5-fold cross-validation. Features were visualized using GCN-Explainer, an ablation study and univariate t-testing. The results showed a mean classification accuracy of 61% for MDD versus controls. Mean accuracy for classifying (non-)medicated subgroups was 62%. Sex classification accuracy was substantially better across datasets (73-81%). Visualization of the results showed that classifications were driven by stronger thalamic connections in both datasets, while nearly all other connections were weaker with small univariate effect sizes. These results suggest that whole brain resting-state connectivity is a reliable though poor biomarker for MDD, presumably due to disease heterogeneity as further supported by the higher accuracy for sex classification using the same methods. Deep learning revealed thalamic hyperconnectivity as a prominent neurophysiological signature of depression in both multicenter studies, which may guide the development of biomarkers in future studies.

Long wavelength light reduces the negative consequences of dim light at night.

Many patients with autism spectrum disorders (ASD) show disturbances in their sleep/wake cycles, and they may be particularly vulnerable to the impact of circadian disruptors. We have previously shown that a 2-weeks exposure to dim light at night (DLaN) disrupts diurnal rhythms, increases repetitive behaviors and reduces social interactions in contactin-associated protein-like 2 knock out (Cntnap2 KO) mice. The deleterious effects of DLaN may be mediated by intrinsically photosensitive retinal ganglion cells (ipRGCs) expressing the photopigment melanopsin, which is maximally sensitive to blue light (480 nm). In this study, the usage of a light-emitting diode array enabled us to shift the spectral properties of the DLaN while keeping the intensity of the illumination at 10 lx. First, we confirmed that the short-wavelength enriched lighting produced strong acute suppression of locomotor activity (masking), robust light-induced phase shifts, and cFos expression in the suprachiasmatic nucleus in wild-type (WT) mice, while the long-wavelength enriched lighting evoked much weaker responses. Opn4DTA mice, lacking the melanopsin expressing ipRGCs, were resistant to DLaN effects. Importantly, shifting the DLaN stimulus to longer wavelengths mitigated the negative impact on the activity rhythms and 'autistic' behaviors (i.e. reciprocal social interactions, repetitive grooming) in the Cntnap2 KO as well as in WT mice. The short-, but not the long-wavelength enriched, DLaN triggered cFos expression in in the basolateral amygdala (BLA) as well as in the peri-habenula region raising that possibility that these cell populations may mediate the effects. Broadly, our findings are consistent with the recommendation that spectral properties of light at night should be considered to optimize health in neurotypical as well as vulnerable populations.