Altered white matter microstructure and neurocognitive function of HIV-infected patients with low nadir CD4.

Altered white matter microstructure has been reported repeatedly using diffusion tensor imaging (DTI) in HIV-associated neurocognitive disorders. However, the associations between neurocognitive deficits and impaired white matter remains obscure due to frequent physical and psychiatric comorbidities in the patients. Severe immune suppression, reflected by low nadir CD4 T-cell counts, is reported to be associated with the neurocognitive deficits in the patients. In the present study, we examined white matter integrity using DTI and tract-based spatial statistics (TBSS), and neurocognitive functions using a battery of tests, in 15 HIV-infected patients with low nadir CD4, 16 HIV-infected patients with high nadir CD4, and 33 age- and sex-matched healthy controls. As DTI measures, we analyzed fractional anisotropy (FA) and mean diffusivity (MD). In addition, we investigated the correlation between white matter impairments and neurocognitive deficits. Among the three participant groups, the patients with low nadir CD4 showed significantly lower performance in processing speed and motor skills, and had significantly increased MD in widespread regions of white matter in both hemispheres. In the patients with low nadir CD4, there was a significant negative correlation between motor skills and MD in the right motor tracts, as well as in the corpus callosum. In summary, this study may provide white matter correlates of neurocognitive deficits in HIV-infected patients with past severe immune suppression as legacy effects.

Extracting Apathy From Depression Syndrome in Traumatic Brain Injury by Using a Clustering Method.

OBJECTIVE: Depression and apathy are common after traumatic brain injury (TBI), and different intervention strategies are recommended for each. However, a differential diagnosis can be difficult in clinical settings, especially given that apathy is considered to be a symptom of depression. In this study, the investigators aimed to isolate apathy from depression among patients with TBI and to examine whether apathy is exclusively associated with the amount of daily activity, as previously reported in the literature. METHODS: Eighty-eight patients with chronic TBI completed the Japanese versions of the 21-item Beck Depression Inventory-II (BDI-II) and the Starkstein Apathy Scale (AS). Daily activity was measured with a 24-hour life log. A hierarchical cluster analysis was applied to divide the BDI-II data into separable components, and components' correlations with results of the AS and 24-hour life log scale were evaluated. RESULTS: The BDI-II and AS revealed that 37 patients (42.0%) had both depression and apathy. BDI-II data were classified into four separate clusters (somatic symptoms, loss of self-worth, affective symptoms, and apathy symptoms). Loss of self-worth and apathy symptoms subscores were significantly positively correlated with total AS score (r=0.32, p=0.002, and r=0.52, p<0.001, respectively). The apathy symptoms subscore was significantly correlated with the amount of daily activity (r=-0.29, p=0.009). CONCLUSIONS: The findings suggest that the BDI-II can differentiate between apathy and depression among patients with TBI, which is essential when selecting intervention options. Moreover, apathy symptoms predicted patients' real-life daily activity.

Deep Learning-Derived High-Level Neuroimaging Features Predict Clinical Outcomes for Large Vessel Occlusion.

Background and Purpose- For patients with large vessel occlusion, neuroimaging biomarkers that evaluate the changes in brain tissue are important for determining the indications for mechanical thrombectomy. In this study, we applied deep learning to derive imaging features from pretreatment diffusion-weighted image data and evaluated the ability of these features in predicting clinical outcomes for patients with large vessel occlusion. Methods- This multicenter retrospective study included patients with anterior circulation large vessel occlusion treated with mechanical thrombectomy between 2013 and 2018. We designed a 2-output deep learning model based on convolutional neural networks (the convolutional neural network model). This model employed encoder-decoder architecture for the ischemic lesion segmentation, which automatically extracted high-level feature maps in its middle layers, and used its information to predict the clinical outcome. Its performance was internally validated with 5-fold cross-validation, externally validated, and the results compared with those from the standard neuroimaging biomarkers Alberta Stroke Program Early CT Score and ischemic core volume. The prediction target was a good clinical outcome, defined as a modified Rankin Scale score at 90-day follow-up of 0 to 2. Results- The derivation cohort included 250 patients, and the validation cohort included 74 patients. The convolutional neural network model showed the highest area under the receiver operating characteristic curve: 0.81±0.06 compared with 0.63±0.05 and 0.64±0.05 for the Alberta Stroke Program Early CT Score and ischemic core volume models, respectively. In the external validation, the area under the curve for the convolutional neural network model was significantly superior to those for the other 2 models. Conclusions- Compared with the standard neuroimaging biomarkers, our deep learning model derived a greater amount of prognostic information from pretreatment neuroimaging data. Although a confirmatory prospective evaluation is needed, the high-level imaging features derived by deep learning may offer an effective prognostic imaging biomarker.

Autistic traits are associated with the functional connectivity of between-but not within-attention systems in the general population.

BACKGROUND: Previous studies have demonstrated that individuals with autism spectrum disorder (ASD) exhibit dysfunction in the three attention systems (i.e., alerting, orienting, and executive control) as well as atypical relationships among these systems. Additionally, other studies have reported that individuals with subclinical but high levels of autistic traits show similar attentional tendencies to those observed in ASD. Based on these findings, it was hypothesized that autistic traits would affect the functions and relationships of the three attention systems in a general population. Resting-state functional magnetic resonance imaging (fMRI) was performed in 119 healthy adults to investigate relationships between autistic traits and within- and between-system functional connectivity (FC) among the three attention systems. Twenty-six regions of interest that were defined as components of the three attention systems by a previous task-based fMRI study were examined in terms of within- and between-system FC. We assessed autistic traits using the Autism-Spectrum Quotient. RESULTS: Correlational analyses revealed that autistic traits were significantly correlated with between-system FC, but not with within-system FC. CONCLUSIONS: Our results imply that a high autistic trait level, even when subclinical, is associated with the way the three attention systems interact.

Predicting Clinical Outcomes of Large Vessel Occlusion Before Mechanical Thrombectomy Using Machine Learning.

Background and Purpose- The clinical course of acute ischemic stroke with large vessel occlusion (LVO) is a multifactorial process with various prognostic factors. We aimed to model this process with machine learning and predict the long-term clinical outcome of LVO before endovascular treatment and to compare our method with previously developed pretreatment scoring methods. Methods- The derivation cohort included 387 LVO patients, and the external validation cohort included 115 LVO patients with anterior circulation who were treated with mechanical thrombectomy. The statistical model with logistic regression without regularization and machine learning algorithms, such as regularized logistic regression, linear support vector machine, and random forest, were used to predict good clinical outcome (modified Rankin Scale score of 0-2 at 90 days) with standard and multiple pretreatment clinical variables. Five previously reported pretreatment scoring methods (the Pittsburgh Response to Endovascular Therapy score, the Stroke Prognostication Using Age and National Institutes of Health Stroke Scale index, the Totaled Health Risks in Vascular Events score, the Houston Intra-Arterial Therapy score, and the Houston Intra-Arterial Therapy 2 score) were compared with these models for the area under the receiver operating characteristic curve. Results- The area under the receiver operating characteristic curve of random forest, which was the worst among the machine learning algorithms, was significantly higher than those of the standard statistical model and the best model among the previously reported pretreatment scoring methods in the derivation (the area under the receiver operating characteristic curve were 0.85±0.07 for random forest, 0.78±0.08 for logistic regression without regularization, and 0.77±0.09 for Stroke Prognostication using Age and National Institutes of Health Stroke Scale) and validation cohorts (the area under the receiver operating characteristic curve were 0.87±0.01 for random forest, 0.56±0.07 for logistic regression without regularization, and 0.83±0.00 for Pittsburgh Response to Endovascular Therapy). Conclusions- Machine learning methods with multiple pretreatment clinical variables can predict clinical outcomes of patients with anterior circulation LVO who undergo mechanical thrombectomy more accurately than previously developed pretreatment scoring methods.

[Overcoming Fear of Death in Home Caregiving].

This study aimed to clarify the factors for reducing the burden of caregivers caring for terminal patients. We conducted semi-structured interviews with bereaved caregivers, seeking those who had felt neither fear nor anxiety during caregiving; their responses were analyzed using the Steps for Coding and Theorization. The one caregiver who showed neither fear nor anxiety(1)believed in an afterlife,(2)sought a physician who would respect her belief,(3)sought respect for her medical decisions, and(4)found distance to the clinic to be a critical factor. Our findings suggest that doctors' respect for caregivers' beliefs may be an important factor in reducing caregiver burden at the end of life.

Silent information regulator 2 homolog 1 counters cerebral hypoperfusion injury by deacetylating endothelial nitric oxide synthase.

BACKGROUND AND PURPOSE: Silent information regulator 2 homolog 1 (SIRT1) is a protein deacetylase that has been reported to suppress neurodegenerative and cardiovascular diseases in model organisms. We hypothesized that neurovascular protection is one of the diverse actions of SIRT1. This study was designed to determine whether SIRT1 protects against the consequences of cerebral hypoperfusion in vivo. METHODS: Sirt1-overexpressing (Sirt1-Tg) mice driven by a prion promoter and their wild-type littermates were subjected to bilateral common carotid artery stenosis using external microcoils. Using Sirt1-Tg mice, we assessed the effect of SIRT1 on cerebral blood flow, cerebral angioarchitecture, histological and ultrastructural changes, and spatial working memory at several time points. We also evaluated the effects of preadministration of SIRT1 inhibitors or endothelial nitric oxide synthase inhibitors on cerebral blood flow after bilateral common carotid artery stenosis in Sirt1-Tg mice. Levels of acetylated and nonacetylated endothelial nitric oxide synthase were measured semiquantitatively with immunoblotting. RESULTS: Cerebral hypoperfusion induced by bilateral common carotid artery stenosis caused memory impairment and histological changes in wild-type littermates. However, these phenotypes were rescued in Sirt1-Tg mice, where cerebral blood flow was maintained even poststenosis. Electron microscopic analyses showed irregularities in the vascular endothelia, such as tight junction openings in wild-type mice, which were absent in Sirt1-Tg littermates. Brain endothelial nitric oxide synthase was acetylated after cerebral hypoperfusion in wild-type littermates but remained unacetylated in Sirt1-Tg mice. Moreover, treatment with SIRT1 inhibitors and endothelial nitric oxide synthase inhibitors abolished the vasculoprotective effects of SIRT1. CONCLUSIONS: Our results indicate that neurovascular endothelial SIRT1 potentiation upregulates the nitric oxide system and counters cerebral hypoperfusion injury. This novel cerebral blood flow-preserving mechanism offers potential molecular targets for future therapeutic intervention.

The Role of Occipitotemporal Network for Speed-Reading: An fMRI Study.

The activity of occipitotemporal regions involved in linguistic reading processes, such as the ventral occipitotemporal cortex (vOT), is believed to exhibit strong interactions during higher-order language processing, specifically in the connectivity between the occipital gyrus and the temporal gyrus. In this study, we utilized functional magnetic resonance imaging (fMRI) with psychophysiological interaction (PPI) and dynamic causal modeling (DCM) to investigate the functional and effective connectivity in the occipitotemporal network during speed reading. We conducted the experiment with native Japanese speakers who underwent and without speed-reading training and subsequently performed established reading tasks at different speeds (slow, medium, and fast) while undergoing 3-Tesla Siemens fMRI. Our activation analyses revealed significant changes in occipital and temporal regions as reading speed increased, indicating functional connectivity within the occipitotemporal network. DCM results further demonstrated more intricate effective connections and high involvement within the occipitotemporal pathway: (1) reading signals originated from the inferior occipital gyrus (iO), distributed to the vOT and the posterior superior temporal sulcus (pSTS), and then gathered in the anterior superior temporal sulcus (aSTS); (2) reading speed loads had modulation effects on the pathways from the aSTS to vOT and from the iO to vOT. These findings highlight the complex connectivity and dynamic interactions within the occipitotemporal network during speed-reading processes.

Validation of Wistar-Kyoto rats kept in solitary housing as an animal model for depression using voxel-based morphometry.

Major depressive disorder is a common psychiatric condition often resistant to medication. The Wistar-Kyoto (WKY) rat has been suggested as an animal model of depression; however, it is still challenging to translate results from animal models into humans. Solitary housing is a mild stress paradigm that can simulate the environment of depressive patients with limited social activity due to symptoms. We used voxel-based morphometry to associate the solitary-housed WKY (sWKY) rat model with data from previous human studies and validated our results with behavioural studies. As a result, atrophy in sWKY rats was detected in the ventral hippocampus, caudate putamen, lateral septum, cerebellar vermis, and cerebellar nuclei (p < 0.05, corrected for family-wise error rate). Locomotor behaviour was negatively correlated with habenula volume and positively correlated with atrophy of the cerebellar vermis. In addition, sWKY rats showed depletion of sucrose consumption not after reward habituation but without reward habituation. Although the application of sWKY rats in a study of anhedonia might be limited, we observed some similarities between the regions of brain atrophy in sWKY rats and humans with depression, supporting the translation of sWKY rat studies to humans.

Discrete prefrontal neuronal circuits determine repeated stress-induced behavioral phenotypes in male mice.

Chronic stress is a major risk factor for psychiatric disorders, including depression. Although depression is a highly heterogeneous syndrome, it remains unclear how chronic stress drives individual differences in behavioral responses. In this study, we developed a subtyping-based approach wherein stressed male mice were divided into four subtypes based on their behavioral patterns of social interaction deficits and anhedonia, the core symptoms of psychiatric disorders. We identified three prefrontal cortical neuronal projections that regulate repeated stress-induced behavioral phenotypes. Among them, the medial prefrontal cortex (mPFC)→anterior paraventricular thalamus (aPVT) pathway determines the specific behavioral subtype that exhibits both social deficits and anhedonia. Additionally, we identified the circuit-level molecular mechanism underlying this subtype: KDM5C-mediated epigenetic repression of Shisa2 transcription in aPVT projectors in the mPFC led to social deficits and anhedonia. Thus, we provide a set of biological aspects at the cellular, molecular, and epigenetic levels that determine distinctive stress-induced behavioral phenotypes.

New objective simple evaluation methods of amyloid PET/CT using whole-brain histogram and Top20%-Map.

OBJECTIVE: This study aims to assess the utility of newly developed objective methods for the evaluation of intracranial abnormal amyloid deposition using PET/CT histogram without use of cortical ROI analyses. METHODS: Twenty-five healthy volunteers (HV) and 38 patients with diagnosed or suspected dementia who had undergone 18F-FPYBF-2 PET/CT were retrospectively included in this study. Out of them, 11C-PiB PET/CT had been also performed in 13 subjects. In addition to the conventional methods, namely visual judgment and quantitative analyses using composed standardized uptake value ratio (comSUVR), the PET images were also evaluated by the following new parameters: the skewness and the mode-to-mean ratio (MMR) obtained from the histogram of the brain parenchyma; Top20%-map highlights the areas with high tracer accumulation occupying 20% volume of the total brain parenchymal on the individual's CT images. We evaluated the utility of the new methods using histogram compared with the visual assessment and comSUVR. The results of these new methods between 18F-FPYBF-2 and 11C-PiB were also compared in 13 subjects. RESULTS: In visual analysis, 32, 9, and 22 subjects showed negative, border, and positive results, and composed SUVR in each group were 1.11 ± 0.06, 1.20 ± 0.13, and 1.48 ± 0.18 (p < 0.0001), respectively. Visually positive subjects showed significantly low skewness and high MMR (p < 0.0001), and the Top20%-Map showed the presence or absence of abnormal deposits clearly. In comparison between the two tracers, visual evaluation was all consistent, and the ComSUVR, the skewness, the MMR showed significant good correlation. The Top20%-Maps showed similar pattern. CONCLUSIONS: Our new methods using the histogram of the brain parenchymal accumulation are simple and suitable for clinical practice of amyloid PET, and Top20%-Map on the individual's brain CT can be of great help for the visual assessment.

Segmentation and Volume Estimation of the Habenula Using Deep Learning in Patients With Depression.

Background: The habenula is involved in the pathophysiology of depression. However, its small structure limits the accuracy of segmentation methods, and the findings regarding its volume have been inconsistent. This study aimed to create a highly accurate habenula segmentation model using deep learning, test its generalizability to clinical magnetic resonance imaging, and examine differences between healthy participants and patients with depression. Methods: This multicenter study included 382 participants (patients with depression: N = 234, women 47.0%; healthy participants: N = 148, women 37.8%). A 3-dimensional residual U-Net was used to create a habenula segmentation model on 3T magnetic resonance images. The reproducibility and generalizability of the predictive model were tested on various validation cohorts. Thereafter, differences between the habenula volume of healthy participants and that of patients with depression were examined. Results: A Dice coefficient of 86.6% was achieved in the derivation cohort. The test-retest dataset showed a mean absolute percentage error of 6.66, indicating sufficiently high reproducibility. A Dice coefficient of >80% was achieved for datasets with different imaging conditions, such as magnetic field strengths, spatial resolutions, and imaging sequences, by adjusting the threshold. A significant negative correlation with age was observed in the general population, and this correlation was more pronounced in patients with depression (p < 10-7, r = -0.59). Habenula volume decreased with depression severity in women even when the effects of age and scanner were excluded (p = .019, η2 = 0.099). Conclusions: Habenula volume could be a pathophysiologically relevant factor and diagnostic and therapeutic marker for depression, particularly in women.

Accurate segmentation of the habenula, a brain region implicated in depression, is challenging. In this study, we developed an automated human habenula segmentation model using deep learning techniques. The model was confirmed to be reproducible and generalizable at various spatial resolutions. Application of this model to a multicenter dataset confirmed that habenula volume decreased with age in healthy volunteers, an association that was more pronounced in individuals with depression. In addition, habenula volume decreased with the severity of depression in women. This novel model for habenula segmentation enables further study of the role of the habenula in depression.

Sustained antidepressant effects of ketamine metabolite involve GABAergic inhibition-mediated molecular dynamics in aPVT glutamatergic neurons.

Despite the rapid and sustained antidepressant effects of ketamine and its metabolites, their underlying cellular and molecular mechanisms are not fully understood. Here, we demonstrate that the sustained antidepressant-like behavioral effects of (2S,6S)-hydroxynorketamine (HNK) in repeatedly stressed animal models involve neurobiological changes in the anterior paraventricular nucleus of the thalamus (aPVT). Mechanistically, (2S,6S)-HNK induces mRNA expression of extrasynaptic GABAA receptors and subsequently enhances GABAA-receptor-mediated tonic currents, leading to the nuclear export of histone demethylase KDM6 and its replacement by histone methyltransferase EZH2. This process increases H3K27me3 levels, which in turn suppresses the transcription of genes associated with G-protein-coupled receptor signaling. Thus, our findings shed light on the comprehensive cellular and molecular mechanisms in aPVT underlying the sustained antidepressant behavioral effects of ketamine metabolites. This study may support the development of potentially effective next-generation pharmacotherapies to promote sustained remission of stress-related psychiatric disorders.

Requirement for hippocampal CA3 NMDA receptors in artificial association of memory events stored in CA3 cell ensembles.

The N-methyl-D-aspartate receptors (NRs) in hippocampal CA3 are crucial for the synaptic transmission and plasticity within the CA3 recurrent circuit, which supports the hippocampal functions, such as pattern completion, and reverberatory association of sensory inputs. Previous study showed that synchronous activation of distinct cell populations in CA3, which correspond to distinct events, associated independent events, suggesting that the recurrent circuit expressing NRs in CA3 mediates the artificial association of memory events stored in CA3 ensembles. However, it is still unclear whether CA3 NRs are crucial for the artificial association of memory events stored in the CA3 ensembles. Here we report that the triple transgenic mice (cfos-tTA/KA1-Cre/NR1 flox/flox), which specifically lack NRs in the CA3 cell ensembles, showed impairment in artificial association between two events, which in control mice triggered artificial association. This result indicates that NRs in the hippocampal CA3 are required for the artificial association of memory events stored in the CA3 cell ensembles.

Correlation between trait emotional intelligence and prefrontal activation during a verbal fluency task: A functional near-infrared spectroscopy study.

Stress is inevitable in humans and stress changes our physical and mental states. Stress has been studied epidemiologically, biologically, and psychologically. First defined in 1990, emotional intelligence (EI) affects psychological stress management. In contrast, the prefrontal cortex (PFC) is suggested to play a vital role in stress management. Human PFC activity can be inferred from the balance of oxygenated and deoxygenated hemoglobin in cerebral blood flow, which can be measured and calculated using functional near-infrared spectroscopy (fNIRS). An important cognitive activation task to activate the PFC is the verbal fluency task (VFT). Therefore, if the PFC is activated by the VFT and monitored by fNIRS, and the activity correlates with EI, fNIRS can be used to measure EI. In this study, Psychological tests using the self-rating depression scale, state-trait anxiety inventory (STAI), and trait emotional intelligence questionnaire-short form (TEIQue-SF) were conducted to evaluate the correlation with VFT performance. Relative oxygenated and deoxygenated hemoglobin concentrations were measured using an fNIRS device, and their correlation with VFT performance was tested. Spearman correlation coefficient was used to determine correlations. Results were as follows. Although VFT performance did not correlate with the oxygenated hemoglobin concentration ([Oxy-Hb]) changes, [Oxy-Hb] was elevated in all channels. VFT performance was significantly negatively correlated with the Zung self-rating depression scale (ρ = 0.063, P = .759), trait anxiety or anxiety level as a personal characteristic of STAI (ρ = 0.243, P = .232), and state anxiety or anxiety about an event of STAI (ρ = -0.138, P = .500), whereas no correlation was found with the TEIQue-SF (ρ = 0.303, P = .132). Healthy individuals PFC activity is not severely affected by their mental state and cognitive activation successfully activates the PFC, supporting the hypothesis that EI is correlated with frontal cortical activation during the VFT in a nonclinical population. EI may play a vital role in reducing stress associated with depression and anxiety in our social lives. Although we failed to show a statistical correlation between TEIQue-SF and [Oxy-Hb] due to a sample size shortage, our preliminary study was the first to attempt to show the PFC activity of EI through a hemodynamic response. Future research may elucidate the role of EI in reducing psychological stress in social life.

Voxel-based clustered imaging by multiparameter diffusion tensor images for predicting the grade and proliferative activity of meningioma.

INTRODUCTION: Meningiomas are the most common primary central nervous system tumors. Predicting the grade and proliferative activity of meningiomas would influence therapeutic strategies. We aimed to apply the multiple parameters from preoperative diffusion tensor images for predicting meningioma grade and proliferative activity. METHODS: Nineteen patients with low-grade meningiomas and eight with high-grade meningiomas were included. For the prediction of proliferative activity, the patients were divided into two groups: Ki-67 monoclonal antibody labeling index (MIB-1 LI) < 5% (lower MIB-1 LI group; n = 18) and MIB-1 LI ≥ 5% (higher MIB-1 LI group; n = 9). Six features, diffusion-weighted imaging, fractional anisotropy, mean, axial, and radial diffusivities, and raw T2 signal with no diffusion weighting, were extracted as multiple parameters from diffusion tensor imaging. The two-level clustering approach for a self-organizing map followed by the K-means algorithm was applied to cluster a large number of input vectors with the six features. We also validated whether the diffusion tensor-based clustered image (DTcI) was helpful for predicting preoperative meningioma grade or proliferative activity. RESULTS: The sensitivity, specificity, accuracy, and area under the curve of receiver operating characteristic curves from the 16-class DTcIs for differentiating high- and low-grade meningiomas were 0.870, 0.901, 0.891, and 0.959, and those from the 10-class DTcIs for differentiating higher and lower MIB-1 LIs were 0.508, 0.770, 0.683, and 0.694, respectively. The log-ratio values of class numbers 13, 14, 15, and 16 were significantly higher in high-grade meningiomas than in low-grade meningiomas (p < .001). With regard to MIB-1 LIs, the log-ratio values of class numbers 8, 9, and 10 were higher in meningiomas with higher MIB-1 groups (p < .05). CONCLUSION: The multiple diffusion tensor imaging-based parameters from the voxel-based DTcIs can help differentiate between low- and high-grade meningiomas and between lower and higher proliferative activities.

A model for estimating the brainstem volume in normal healthy individuals and its application to diffuse axonal injury patients.

Diffuse axonal injury (DAI) is a subtype of traumatic brain injury that causes acute-phase consciousness disorders and widespread chronic-phase brain atrophy. Considering the importance of brainstem damage in DAI, a valid method for evaluating brainstem volume is required. We obtained volume measurements from 182 healthy adults by analyzing T1-weighted magnetic resonance images, and created an age-/sex-/intracranial volume-based quantitative model to estimate the normal healthy volume of the brainstem and cerebrum. We then applied this model to the volume measurements of 22 DAI patients, most of whom were in the long-term chronic phase and had no gross focal injury, to estimate the percentage difference in volume from the expected normal healthy volume in different brain regions, and investigated its association with the duration of posttraumatic amnesia (which is an early marker of injury severity). The average loss of the whole brainstem was 13.9%. Moreover, the percentage loss of the whole brainstem, and particularly of the pons and midbrain, was significantly negatively correlated with the duration of posttraumatic amnesia. Our findings suggest that injury severity, as denoted by the duration of posttraumatic amnesia, is among the factors affecting the chronic-phase brainstem volume in patients with DAI.

Information flow and dynamic functional connectivity during electroconvulsive therapy in patients with depression.

BACKGROUND: Electroconvulsive therapy is effectively used for treatment-resistant depression; however, its neural mechanism is largely unknown. Resting-state functional magnetic resonance imaging is promising for monitoring outcomes of electroconvulsive therapy for depression. This study aimed to explore the imaging correlates of the electroconvulsive therapy effects on depression using Granger causality analysis and dynamic functional connectivity analyses. METHODS: We performed advanced analyses of resting-state functional magnetic resonance imaging data at the beginning and intermediate stages and end of the therapeutic course to identify neural markers that reflect or predict the therapeutic effects of electroconvulsive therapy on depression. RESULTS: We demonstrated that information flow between the functional networks analyzed by Granger causality changes during electroconvulsive therapy, and this change was correlated with the therapeutic outcome. Information flow and the dwell time (an index reflecting the temporal stability of functional connectivity) before electroconvulsive therapy are correlated with depressive symptoms during and after treatment. LIMITATIONS: First, the sample size was small. A larger group is needed to confirm our findings. Second, the influence of concomitant pharmacotherapy on our results was not fully addressed, although we expected it to be minimal because only minor changes in pharmacotherapy occurred during electroconvulsive therapy. Third, different scanners were used the groups, although the acquisition parameters were the same; a direct comparison between patient and healthy participant data was not possible. Thus, we presented the data of the healthy participants separately from that of the patients as a reference. CONCLUSIONS: These results show the specific properties of functional brain connectivity.

GPCR-mediated calcium and cAMP signaling determines psychosocial stress susceptibility and resiliency.

Chronic stress increases the risk of developing psychiatric disorders, including mood and anxiety disorders. Although behavioral responses to repeated stress vary across individuals, the underlying mechanisms remain unclear. Here, we perform a genome-wide transcriptome analysis of an animal model of depression and patients with clinical depression and report that dysfunction of the Fos-mediated transcription network in the anterior cingulate cortex (ACC) confers a stress-induced social interaction deficit. Critically, CRISPR-Cas9-mediated ACC Fos knockdown causes social interaction deficits under stressful situation. Moreover, two classical second messenger pathways, calcium and cyclic AMP, in the ACC during stress differentially modulate Fos expression and regulate stress-induced changes in social behaviors. Our findings highlight a behaviorally relevant mechanism for the regulation of calcium- and cAMP-mediated Fos expression that has potential as a therapeutic target for psychiatric disorders related to stressful environments.

Differences between subclinical attention-deficit/hyperactivity and autistic traits in default mode, salience, and frontoparietal network connectivities in young adult Japanese.

Attention-deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are associated with attentional impairments, with both commonalities and differences in the nature of their attention deficits. This study aimed to investigate the neural correlates of ADHD and ASD traits in healthy individuals, focusing on the functional connectivity (FC) of attention-related large-scale brain networks (LSBNs). The participants were 61 healthy individuals (30 men; age, 21.9 ± 1.9 years). The Adult ADHD Self-Report Scale (ASRS) and Autism Spectrum Quotient (AQ) were administered as indicators of ADHD and ASD traits, respectively. Performance in the continuous performance test (CPT) was used as a behavioural measure of sustained attentional function. Functional magnetic resonance imaging scans were performed during the resting state (Rest) and auditory oddball task (Odd). Considering the critical role in attention processing, we focused our analyses on the default mode (DMN), frontoparietal (FPN), and salience (SN) networks. Region of interest (ROI)-to-ROI analyses (false discovery rate < 0.05) were performed to determine relationships between psychological measures with within-network FC (DMN, FPN, and SN) as well as with between-network FC (DMN-FPN, DMN-SN, and FPN-SN). ASRS scores, but not AQ scores, were correlated with less frequent commission errors and shorter reaction times in the CPT. During Odd, significant positive correlations with ASRS were demonstrated in multiple FCs within DMN, while significant positive correlations with AQ were demonstrated in multiple FCs within FPN. AQs were negatively correlated with FPN-SN FCs. During Rest, AQs were negatively and positively correlated with one FC within the SN and multiple FCs between the DMN and SN, respectively. These findings of the ROI-to-ROI analysis were only partially replicated in a split-half replication analysis, a replication analysis with open-access data sets, and a replication analysis with a structure-based atlas. The better CPT performance by individuals with subclinical ADHD traits suggests positive effects of these traits on sustained attention. Differential associations between LSBN FCs and ASD/ADHD traits corroborate the notion of differences in sustained and selective attention between clinical ADHD and ASD.