Differentiation between multifocal CNS lymphoma and glioblastoma based on MRI criteria.

PURPOSE: Differentiating between glioblastoma (GB) with multiple foci (mGB) and multifocal central nervous system lymphoma (mCNSL) can be challenging because these cancers share several features at first appearance on magnetic resonance imaging (MRI). The aim of this study was to explore morphological differences in MRI findings for mGB versus mCNSL and to develop an interpretation algorithm with high diagnostic accuracy. METHODS: In this retrospective study, MRI characteristics were compared between 50 patients with mGB and 50 patients with mCNSL treated between 2015 and 2020. The following parameters were evaluated: size, morphology, lesion location and distribution, connections between the lesions on the fluid-attenuated inversion recovery sequence, patterns of contrast enhancement, and apparent diffusion coefficient (ADC) values within the tumor and the surrounding edema, as well as MR perfusion and susceptibility weighted imaging (SWI) whenever available. RESULTS: A total of 187 mCNSL lesions and 181 mGB lesions were analyzed. The mCNSL lesions demonstrated frequently a solid morphology compared to mGB lesions, which showed more often a cystic, mixed cystic/solid morphology and a cortical infiltration. The mean measured diameter was significantly smaller for mCNSL than mGB lesions (p < 0.001). Tumor ADC ratios were significantly smaller in mCNSL than in mGB (0.89 ± 0.36 vs. 1.05 ± 0.35, p < 0.001). The ADC ratio of perilesional edema was significantly higher (p < 0.001) in mCNSL than in mGB. In SWI / T2*-weighted imaging, tumor-associated susceptibility artifacts were more often found in mCNSL than in mGB (p < 0.001). CONCLUSION: The lesion size, ADC ratios of the lesions and the adjacent tissue as well as the vascularization of the lesions in the MR-perfusion were found to be significant distinctive patterns of mCNSL and mGB allowing a radiological differentiation of these two entities on initial MRI. A diagnostic algorithm based on these parameters merits a prospective validation.

Improving Prefrontal Oxygenation and Cardiac Autonomic Activity Following Meditation: A Functional Near-Infrared Spectroscopy (fNIRS) Study.

OBJECTIVE: The empirical evidence explicitly demonstrates that meditation practice enhances both brain functions and mental well-being. A meditative relaxation approach called the mind sound resonance technique (MSRT) has shown promising effects on children, adolescents, and people with psychological illnesses. This study aimed to investigate the effects of MSRT practice on brain hemodynamics, heart rate variability (HRV), mindfulness, and anxiety levels in college students. METHODS: Fifty volunteers in all genders (females, n = 30; males, n = 20) aged between 19 and 30 years were chosen from an educational institute and allocated into two groups, i.e., MSRT (n = 25) and supine rest (SR; n = 25). Enrolled participants were measured cerebral hemodynamics and HRV before, during, and after the MSRT or SR practice. The self-reported assessments including state anxiety and mindfulness were assessed before and after the intervention. RESULTS: The results demonstrated that practicing MSRT significantly improved oxygenation (p < 0.05) in the right prefrontal cortex (PFC) and increased low-frequency (LF) (p < 0.05) and decreased high-frequency (HF) (p < 0.05) component of HRV when compared to the baseline. The between-group analysis showed a significant difference between MSRT and SR in the standard deviation of the normal-to-normal (SDNN) (p < 0.05) component of HRV. CONCLUSION: These crumbs of evidence imply that MSRT sessions may foster the development of anxiety-related coping skills by elevating mindfulness, promoting PFC oxygenation, and modulating HRV in MSRT practitioners.

Bayesian Lesion Estimation with a Structured Spike-and-Slab Prior.

Neural demyelination and brain damage accumulated in white matter appear as hyperintense areas on T2-weighted MRI scans in the form of lesions. Modeling binary images at the population level, where each voxel represents the existence of a lesion, plays an important role in understanding aging and inflammatory diseases. We propose a scalable hierarchical Bayesian spatial model, called BLESS, capable of handling binary responses by placing continuous spike-and-slab mixture priors on spatially-varying parameters and enforcing spatial dependency on the parameter dictating the amount of sparsity within the probability of inclusion. The use of mean-field variational inference with dynamic posterior exploration, which is an annealing-like strategy that improves optimization, allows our method to scale to large sample sizes. Our method also accounts for underestimation of posterior variance due to variational inference by providing an approximate posterior sampling approach based on Bayesian bootstrap ideas and spike-and-slab priors with random shrinkage targets. Besides accurate uncertainty quantification, this approach is capable of producing novel cluster size based imaging statistics, such as credible intervals of cluster size, and measures of reliability of cluster occurrence. Lastly, we validate our results via simulation studies and an application to the UK Biobank, a large-scale lesion mapping study with a sample size of 40,000 subjects.

Using mechanistic knowledge to appraise contemporary approaches to the rehabilitation of upper limb function following stroke.

It is a paradox of neurological rehabilitation that, in an era in which preclinical models have produced significant advances in our mechanistic understanding of neural plasticity, there is inadequate support for many therapies recommended for use in clinical practice. When the goal is to estimate the probability that a specific form of therapy will have a positive clinical effect, the integration of mechanistic knowledge (concerning 'the structure or way of working of the parts in a natural system') may improve the quality of inference. This is illustrated by analysis of three contemporary approaches to the rehabilitation of lateralized dysfunction affecting people living with stroke: constraint-induced movement therapy; mental practice; and mirror therapy. Damage to 'cross-road' regions of the structural (white matter) brain connectome generates deficits that span multiple domains (motor, language, attention and verbal/spatial memory). The structural integrity of these regions determines not only the initial functional status, but also the response to therapy. As structural disconnection constrains the recovery of functional capability, 'disconnectome' modelling provides a basis for personalized prognosis and precision rehabilitation. It is now feasible to refer a lesion delineated using a standard clinical scan to a (dis)connectivity atlas derived from the brains of other stroke survivors. As the individual disconnection pattern thus obtained suggests the functional domains most likely be compromised, a therapeutic regimen can be tailored accordingly. Stroke is a complex disorder that burdens individuals with distinct constellations of brain damage. Mechanistic knowledge is indispensable when seeking to ameliorate the behavioural impairments to which such damage gives rise.

Diaminonaphthalene Boronic Acid (DANBA): New Approach for Peroxynitrite Sensing Site.

Selective detection of reactive oxygen species (ROS) is vital for studying their role in brain diseases. Fluorescence probes can distinguish ONOO- species from other ROS; however, their selectivity toward ONOO- species depends on the ONOO- recognition group. Aryl-boronic acids and esters, which are common ONOO- recognition groups, are not selective for ONOO- over H2O2. In this study, we developed a diaminonaphthalene (DAN)-protected boronic acid as a new ONOO- recognition group that selectively reacts with ONOO- over H2O2 and other ROS. Three DAN-protected boronic acid (DANBA)-based fluorophores that emit fluorescence over visible to near-infrared (NIR) regions, Cou-BN, BVP-BN, and HDM-BN, and their aryl-boronic acid-based counterparts (Cou-BO, BVP-BO, and HDM-BO), were developed. The DANBA-based probes exhibited enhanced selectivity toward ONOO- over that of their control group, as well as universality in MTT assays and in vitro experiments with PC12 cells. The NIR-emissive HDM-BN was optimized to delineate in vivo ONOO- levels in mouse brains with Parkinson's disease. This DAN-protected boronic acid belongs to a new generation of recognition groups for developing ONOO- probes, and this strategy could be extended to other common hydroxyl-containing dyes to detect ONOO- levels in complex biological systems and processes.

Gene expression and brain imaging association study reveals gene signatures in major depressive disorder.

Major depressive disorder is often characterized by changes in the structure and function of the brain, which are influenced by modifications in gene expression profiles. How the depression-related genes work together within the scope of time and space to cause pathological changes remains unclear. By integrating the brain-wide gene expression data and imaging data in major depressive disorder, we identified gene signatures of major depressive disorder and explored their temporal-spatial expression specificity, network properties, function annotations and sex differences systematically. Based on correlation analysis with permutation testing, we found 345 depression-related genes significantly correlated with functional and structural alteration of brain images in major depressive disorder and separated them by directional effects. The genes with negative effect for grey matter density and positive effect for functional indices are enriched in downregulated genes in the post-mortem brain samples of patients with depression and risk genes identified by genome-wide association studies than genes with positive effect for grey matter density and negative effect for functional indices and control genes, confirming their potential association with major depressive disorder. By introducing a parameter of dispersion measure on the gene expression data of developing human brains, we revealed higher spatial specificity and lower temporal specificity of depression-related genes than control genes. Meanwhile, we found depression-related genes tend to be more highly expressed in females than males, which may contribute to the difference in incidence rate between male and female patients. In general, we found the genes with negative effect have lower network degree, more specialized function, higher spatial specificity, lower temporal specificity and more sex differences than genes with positive effect, indicating they may play different roles in the occurrence and development of major depressive disorder. These findings can enhance the understanding of molecular mechanisms underlying major depressive disorder and help develop tailored diagnostic and treatment strategies for patients of depression of different sex.

Emerging targets for positron emission tomography imaging in proteinopathies.

Positron emission tomography (PET) imaging of neurodegenerative disease has historically focused on a small number of established targets. The development of selective PET radiotracers for novel biological targets enables new ways to interrogate the neuropathology of proteinopathies and will advance our understanding of neurodegeneration. This perspective aims to highlight recent PET radiotracers developed for five emerging targets in proteinopathies (i.e., mHTT, BACE1, TDP-43, OGA, and CH24H).

Correlation between semiautomated magnetic resonance imaging volumetry of the cingulate gyrus and interictal epileptiform discharge lateralization in dogs with idiopathic epilepsy.

BACKGROUND: Brain imaging suggests the involvement of the limbic system, particularly the cingulate gyrus (GC), in dogs with idiopathic epilepsy (IE). HYPOTHESIS: A correlation exists between the side of interictal epileptiform discharges (IEDs) and the volume of the ipsilateral GC (GCe) in dogs with IE. ANIMALS: Dogs admitted to the neurological consultation (32 with epileptic seizures and 13 control) were included. METHODS: This retrospective, blinded study followed the International Veterinary Epilepsy Task Force recommendations for diagnosing IE at the Tier III confidence level. The IE group included 18 and 14 dogs with IEDs in the left and right hemispheres, respectively (median age: 36 months, median weight: 19.5 kg), whereas the control group included 13 dogs (median age: 32 months, median weight: 20 kg). Whole-brain and GC-volumetric assessments were performed by a semiautomated method. RESULTS: In the control group, the volume of the GC was: left, from 743.63 to 1001.61 mm3, right, from 789.35 to 1015.86 mm3. In the study group, the volume of the GC was: left, from 720.88 to 1054.9 mm3 and right, from 566.29 to 987.77 mm3. In dogs with IE, GCe volume was significantly lower than the mean volume of the GC in the control group relative to total intracranial volume (TIV; P = .00044). CONCLUSIONS AND CLINICAL IMPORTANCE: Alterations in the volume of the GC provide insights into structural changes during IE. The use of semiautomatic volumetry provides an advantage by reducing the potential for human error.

Shaping the RF Transmit Field in 7T MRI Using a Nonuniform Metasurface Constructed of Short Conducting Strips.

The ability of metamaterial structures to offer unique properties and new solutions has opened new avenues in a wide range of applications, including super-resolution in optics and efficient antennas in radiofrequency (RF) engineering. In magnetic resonance imaging (MRI), metamaterials hold the promise of increasing the RF magnetic field intensity while minimizing power deposition. Here, we propose a metasurface based on a two-dimensional (2D) array of short conducting strips combined with a high dielectric substrate, which was tuned to operate at ultrahigh field 7T human MRI. While studied in optics and electromagnetics in the GHz-to-THz range, this study is the first to design such a metasurface for proton imaging at 7T MRI. We performed electromagnetic (EM) simulation of the brain MRI setup with the new metasurface placed in the proximity to the temporal lobe, which showed 2.2-fold local increase in the RF transmit efficiency, with superior performance than an array of electric dipoles. In this study, we also investigate the effect of the spatial distribution of the subunits to control the target RF field's distribution. While the common design is based on a uniform distribution of the subunits, nonuniform distribution, such as a denser center (convex) or more condensed edges (concave), provides an extra dimension to tailor both the magnetic and electric fields. The concave distribution achieved 1.5-1.8-fold reduction in the power deposition compared to the uniform distribution in the brain MRI setups examined.

An end-to-end deep learning pipeline to derive blood input with partial volume corrections for automated parametric brain PET mapping.

Dynamic 2-[18F] fluoro-2-deoxy-D-glucose positron emission tomography (dFDG-PET) for human brain imaging has considerable clinical potential, yet its utilization remains limited. A key challenge in the quantitative analysis of dFDG-PET is characterizing a patient-specific blood input function, traditionally reliant on invasive arterial blood sampling. This research introduces a novel approach employing non-invasive deep learning model-based computations from the internal carotid arteries (ICA) with partial volume (PV) corrections, thereby eliminating the need for invasive arterial sampling. We present an end-to-end pipeline incorporating a 3D U-Net based ICA-net for ICA segmentation, alongside a Recurrent Neural Network (RNN) based MCIF-net for the derivation of a model-corrected blood input function (MCIF) with PV corrections. The developed 3D U-Net and RNN was trained and validated using a 5-fold cross-validation approach on 50 human brain FDG PET scans. The ICA-net achieved an average Dice score of 82.18% and an Intersection over Union of 68.54% across all tested scans. Furthermore, the MCIF-net exhibited a minimal root mean squared error of 0.0052. The application of this pipeline to ground truth data for dFDG-PET brain scans resulted in the precise localization of seizure onset regions, which contributed to a successful clinical outcome, with the patient achieving a seizure-free state after treatment. These results underscore the efficacy of the ICA-net and MCIF-net deep learning pipeline in learning the ICA structure's distribution and automating MCIF computation with PV corrections. This advancement marks a significant leap in non-invasive neuroimaging.

Brain mechanisms discriminating enactive mental simulations of running and plogging.

Enactive cognition emphasizes co-constructive roles of humans and their environment in shaping cognitive processes. It is specifically engaged in the mental simulation of behaviors, enhancing the connection between perception and action. Here we investigated the core network of brain regions involved in enactive cognition as applied to mental simulations of physical exercise. We used a neuroimaging paradigm in which participants (N = 103) were required to project themselves running or plogging (running while picking-up litter) along an image-guided naturalistic trail. Using both univariate and multivariate brain imaging analyses, we find that a broad spectrum of brain activation discriminates between the mental simulation of plogging versus running. Critically, we show that self-reported ratings of daily life running engagement and the quality of mental simulation (how well participants were able to imagine themselves running) modulate the brain reactivity to plogging versus running. Finally, we undertook functional connectivity analyses centered on the insular cortex, which is a key region in the dynamic interplay between neurocognitive processes. This analysis revealed increased positive and negative patterns of insular-centered functional connectivity in the plogging condition (as compared to the running condition), thereby confirming the key role of the insular cortex in action simulation involving complex sets of mental mechanisms. Taken together, the present findings provide new insights into the brain networks involved in the enactive mental simulation of physical exercise.

Association of Unc-51-like Kinase 4 (ULK4) with the reactivity of the extended reward system in response to conditioned stimuli.

OBJECTIVES: ULK4 is an established candidate gene for mental disorders and antipsychotic treatment response. We investigated the association of functional genetic variation at the ULK4 locus with the human extended dopaminergic reward system using fMRI during the performance of a well-established reward paradigm. METHODS: Two hundred and thirty-four patients were included in this study. Association of genetic variation in the ULK4 gene with reward system functioning were determined using the Desire-Reason-Dilemma (DRD) paradigm which allows to assess brain activation in response to conditioned reward stimuli. RESULTS: Variant prioritisation revealed the strongest functional signatures for the ULK4 variant rs17215589, coding for amino acid exchange Ala715Thr. For rs17215589 minor allele carriers, we detected increased activation responses to conditioned reward stimuli in the ventral tegmental area, nucleus accumbens and several cortical brain regions of the extended reward system. CONCLUSIONS: Our findings provide further evidence in humans that genetic variation in ULK4 may increase the vulnerability to mental disorders, by modulating the extended reward system function. Future studies are needed to confirm the modulation of the extended reward system by ULK4 and to specify the role of this mechanism in the pathogenesis of psychiatric disorders.

[Prediction and timely identification of postpartum depression: results of the longitudinal RiPoD study in the context of the literature]. / Vorhersage und frühzeitige Identifikation einer postpartalen Depression: Ergebnisse der longitudinalen RiPoD-Studie im Kontext der Literatur.

The first 4-6 weeks after childbirth are defined as the onset time for postpartum depression (PPD). Despite this known time frame there are significant gaps in the identification and treatment of PPD. The risk for postpartum depression (RiPoD) study investigated specific risk factors and predictors of postpartum psychological adjustment processes and the results are presented within the framework of a state of the art review of research. The dynamic neuroplastic changes in the maternal brain during pregnancy and the postpartum period appear to be closely linked to peripartum hormone fluctuations, which jointly influence the development of postpartum mood disorders. Hormonal risk factors such as baby blues and premenstrual syndrome have been found to have a bearing on PPD. The combination of these two factors predicts the risk of PPD with 83% sensitivity within the first week postpartum. Follow-up digital monitoring of symptom development in the first 6 weeks postpartum has enabled an accurate identification of women with PPD. Understanding the interaction between hormone fluctuations, neuroplasticity and psychiatric disorders should be an important target for future research. Early identification and diagnosis of PPD can be easily integrated into the clinical routine and everyday life.

Application of diceCT to Study the Development of the Zika Virus-Infected Mouse Brain.

Zika virus (ZIKV) impacts the developing brain. Here, a technique was applied to define, in 3D, developmental changes in the brains of ZIKV-infected mice. Postnatal day 1 mice were uninfected or ZIKV-infected, then analysed by iodine staining and micro-CT scanning (diffusible iodine contrast-enhanced micro-CT; diceCT) at 3-, 6-, and 10-days post-infection (dpi). Multiple brain regions were visualised using diceCT: the olfactory bulb, cerebrum, hippocampus, midbrain, interbrain, and cerebellum, along with the lens and retina of the eye. Brain regions were computationally segmented and quantitated, with increased brain volumes and developmental time in uninfected mice. Conversely, in ZIKV-infected mice, no quantitative differences were seen at 3 or 6 dpi when there were no clinical signs, but qualitatively, diverse visual defects were identified at 6-10 dpi. By 10 dpi, ZIKV-infected mice had significantly lower body weight and reduced volume of brain regions compared to 10 dpi-uninfected or 6 dpi ZIKV-infected mice. Nissl and immunofluorescent Iba1 staining on post-diceCT tissue were successful, but RNA extraction was not. Thus, diceCT shows utility for detecting both 3D qualitative and quantitative changes in the developing brain of ZIKV-infected mice, with the benefit, post-diceCT, of retaining the ability to apply traditional histology and immunofluorescent analysis to tissue.

Shifting concepts of autoregulation: Commentary to 'static autoregulation in humans'.

Advances in imaging techniques have transformed our understanding of cerebral autoregulation. Older imaging techniques provided measurements of cerebral blood flow (CBF) that reflected the average CBF over a window of 10-20 minutes. A key finding, dating back to 1959, was that CBF remained more or less stable over a remarkably wide range of changes in blood pressure. Modern techniques can measure changes in CBF within the time frame of a heartbeat. They have revealed, paradoxically, a remarkable instability of CBF. This commentary attempts to reconcile these seemingly contradictory observations.

Emerging frontiers in Chronic Traumatic Encephalopathy: early diagnosis and implications for neurotherapeutic interventions.

INTRODUCTION: Chronic Traumatic Encephalopathy (CTE) is a neurodegenerative disorder associated with repetitive head trauma. Historically, the diagnosis has been primarily clinical, which has hindered definitive early diagnosis and proactive intervention. AREAS COVERED: The authors analyze the recent advancements in early diagnosis of CTE by examining biomarkers, imaging, and clinical decision tools. They discuss the identification of neuropathologies - such as tau aggregates - through novel techniques ranging from blood sampling and to brain density scanning. The reader will walk away with a better understanding of current advancements in early detection and be better equipped to deal with encephalopathies secondary to trauma in clinical practice. EXPERT OPINION: Tremendous progress has been made in understanding the pathophysiology of CTE. Despite these advancements, CTE treatment is still primarily symptomatic rather than underlying disease. Future research should focus on integrating current understanding of CTE pathophysiology with treatment modalities.

Yearly change in air pollution and brain aging among older adults: A community-based study in Taiwan.

BACKGROUND: Air pollution is recognized as a modifiable risk factor for dementia, and recent evidence suggests that improving air quality could attenuate cognitive decline and reduce dementia risk. However, studies have yet to explore the effects of improved air quality on brain structures. This study aims to investigate the impact of air pollution reduction on cognitive functions and structural brain differences among cognitively normal older adults. METHODS: Four hundred and thirty-one cognitively normal older adults were from the Epidemiology of Mild Cognitive Impairment study in Taiwan (EMCIT), a community-based cohort of adults aged 60 and older, between year 2017- 2021. Annual concentrations of PM2.5, NO2, O3, and PM10 at participants' residential addresses during the 10 years before enrollment were estimated using ensemble mixed spatial models. The yearly rate of change (slope) in air pollutants was estimated for each participant. Cognitive functions and structural brain images were collected during enrollment. The relationships between the rate of air pollution change and cognitive functions were examined using linear regression models. For air pollutants with significant findings in relation to cognitive function, we further explored the association with brain structure. RESULTS: Overall, all pollutant concentrations, except O3, decreased over the 10-year period. The yearly rates of change (slopes) in PM2.5 and NO2 were correlated with better attention (PM2.5: r = -0.1, p = 0.047; NO2: r = -0.1, p = 0.03) and higher white matter integrity in several brain regions. These regions included anterior thalamic radiation, superior longitudinal fasciculus, inferior longitudinal fasciculus, corticospinal tract, and inferior fronto-occipital fasciculus. CONCLUSIONS: Greater rate of reduction in air pollution was associated with better attention and attention-related white matter integrity. These results provide insight into the mechanism underlying the relationship between air pollution, brain health, and cognitive aging among older adults.

Associations between brain imaging-derived phenotypes and cognitive functions.

We aimed to evaluate the potential causal relationship between brain imaging-derived phenotypes and cognitive functions via Mendelian randomization analyses. Genetic instruments for 470 brain imaging-derived phenotypes were selected from a genome-wide association study based on the UK Biobank (n = 33,224). Statistics for cognitive functions were obtained from the genome-wide association study based on the UK Biobank. We used the inverse variance weighted Mendelian randomization method to investigate the associations between brain imaging-derived phenotypes and cognitive functions, and reverse Mendelian randomization analyses were performed for significant brain imaging-derived phenotypes to examine the reverse causation for the identified associations. We identified three brain imaging-derived phenotypes to be associated with verbal-numerical reasoning, including cortical surface area of the left fusiform gyrus (beta, 0.18 [95% confidence interval, 0.11 to 0.25], P = 4.74 × 10-7), cortical surface area of the right superior temporal gyrus (beta, 0.25 [95% confidence interval, 0.15 to 0.35], P = 6.30 × 10-7), and orientation dispersion in the left superior longitudinal fasciculus (beta, 0.14 [95% confidence interval, 0.09 to 0.20], P = 8.37 × 10-7). The reverse Mendelian randomization analysis indicated that verbal-numerical reasoning had no effect on these three brain imaging-derived phenotypes. This Mendelian randomization study identified cortical surface area of the left fusiform gyrus, cortical surface area of the right superior temporal gyrus, and orientation dispersion in the left superior longitudinal fasciculus as predictors of verbal-numerical reasoning.

Association between irritable temperament and glucose metabolism in the left insula and the right cerebellum.

INTRODUCTION: Affective temperaments are assumed to have biological and neural bases. In the present study, we analyzed 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) images of healthy participants to explore the neural basis of affective temperaments. METHOD: We utilized data of affective temperament measured by the Temperament Evaluation of Memphis, Pisa, Paris, and San Diego-Autoquestionnaire and 18F-FDG PET images of healthy participants from two of our previous studies. A multiple regression analysis was performed to assess the association between 18F-FDG uptake and temperament scores using Statistical Parametric Mapping 12. RESULTS: The final sample included 62 healthy participants. Whole-brain analysis revealed a cluster of 18F-FDG uptake that was significantly and positively associated with irritable temperament scores in the right cerebellum (Crus II, VIII, and IX). After further adjustment for the other four temperament scores, whole-brain analysis revealed a cluster of 18F-FDG uptake significantly and positively associated with irritable temperament scores in the left insula and right cerebellum (Crus II, VIII, and IX). However, no significant association was found between 18F-FDG uptake and the other four temperaments (depressive, cyclothymic, hyperthymic, and anxious). CONCLUSIONS: The left insula and right cerebellum of the cerebrocerebellar circuit may be one of the neural bases of irritable temperament.