Single Brain Research Articles

Alcohol Use Disorder–Associated DNA Methylation in the Nucleus Accumbens and Dorsolateral Prefrontal Cortex

BackgroundAlcohol use disorder (AUD) has a profound public health impact. However, understanding of the molecular mechanisms that underlie the development and progression of AUD remains limited. Here, we investigated AUD-associated DNA methylation changes within and across 2 addiction-relevant brain regions, the nucleus accumbens and dorsolateral prefrontal cortex. MethodsIllumina HumanMethylation EPIC array data from 119 decedents (61 cases, 58 controls) were analyzed using robust linear regression with adjustment for technical and biological variables. Associations were characterized using integrative analyses of public annotation data and published genetic and epigenetic studies. We also tested for brain region–shared and brain region–specific associations using mixed-effects modeling and assessed implications of these results using public gene expression data from human brain. ResultsAt a false discovery rate of ≤.05, we identified 105 unique AUD-associated CpGs (annotated to 120 genes) within and across brain regions. AUD-associated CpGs were enriched in histone marks that tag active promoters, and our strongest signals were specific to a single brain region. Some concordance was found between our results and those of earlier published alcohol use or dependence methylation studies. Of the 120 genes, 23 overlapped with previous genetic associations for substance use behaviors, some of which also overlapped with previous addiction-related methylation studies. ConclusionsOur findings identify AUD-associated methylation signals and provide evidence of overlap with previous genetic and methylation studies. These signals may constitute predisposing genetic differences or robust methylation changes associated with AUD, although more work is needed to further disentangle the mechanisms that underlie these associations and their implications for AUD.

Methadone directly impairs central nervous system cells in vitro

Methadone is a synthetic long-acting opioid that is increasingly used in the replacement therapy of opioid-addicted patients, including pregnant women. However, methadone therapy in this population poses challenges, as it induces cognitive and behavioral impairments in infants exposed to this opioid during prenatal development. In animal models, prenatal methadone exposure results in detrimental consequences to the central nervous system, such as: (i) increased neuronal apoptosis; (ii) disruption of oligodendrocyte maturation and increased apoptosis and (iii) increased microglia and astrocyte activation. However, it remains unclear whether these deleterious effects result from a direct effect of methadone on brain cells. Therefore, our goal was to uncover the impact of methadone on single brain cell types in vitro. Primary cultures of rat neurons, oligodendrocytes, microglia, and astrocytes were treated for three days with 10 µM methadone to emulate a chronic administration. Apoptotic neurons were identified by cleaved caspase-3 detection, and synaptic density was assessed by the juxtaposition of presynaptic and postsynaptic markers. Apoptosis of oligodendrocyte precursors was determined by cleaved caspase-3 detection. Oligodendrocyte myelination was assessed by immunofluorescence, while microglia and astrocyte proinflammatory activation were assessed by both immunofluorescence and RT-qPCR. Methadone treatment increased neuronal apoptosis and reduced synaptic density. Furthermore, it led to increased oligodendrocyte apoptosis and a reduction in the myelinating capacity of these cells, and promoted the proinflammatory activation of microglia and astrocytes. We showed that methadone, the most widely used drug in opioid replacement therapy for pregnant women with opioid addiction, directly impairs brain cells in vitro, highlighting the need for developing alternative therapies to address opioid addiction in this population.

PARP Inhibitors in Brain Metastases from Epithelial Ovarian Cancer through a Multimodal Patient Journey: Case Reports and Literature Review.

Epithelial ovarian cancer (EOC) is the deadliest gynecological malignancy worldwide. Brain metastasis (BM) is quite an uncommon presentation. However, the likelihood of central nervous system (CNS) metastasization should be considered in the context of disseminated disease. The therapeutic management of BMs is an unmet clinical need, to date. We identified, across different cancer centers, six cases of both BRCA wild-type and BRCA-mutated EOCs spreading to the CNS. They presented either with a single brain lesion or with multiple lesions and most of them had intracranial-only disease. All cases received Poly-ADP ribose polymerase inhibitor (PARPi) maintenance, as per clinical practice, for a long time within a multimodal treatment approach. We also provide an insight into the available body of work regarding the management of this intriguing disease setting, with a glimpse of future therapeutic challenges. Despite the lack of unanimous guidelines, multimodal care pathways should be encouraged for the optimal disease control of this unfortunate patient subset. Albeit not being directly investigated in BM patients, PARPi maintenance is deemed to have a valuable role in this setting. Prospective research, aimed to implement worthwhile strategies in the multimodal patient journey of BMs from EOC, is eagerly awaited.

Ultimate-Humeanism

ABSTRACT Super-Humeans (Esfeld and Deckert, 2017. A Minimalist Ontology of the Natural World. New York: Routledge) argue that the most parsimonious ontology of the natural world compatible with our best physical theories consists exclusively of particles and the distance relations between them. This paper argues by contrast that Super-Humean reduction goes insufficiently far, by showing there to be a more parsimonious ontology compatible with physics: Ultimate-Humeanism. This novel view posits an ontology consisting solely of the particles and distance relations required for the existence of a single brain. Super-Humeans impose conditions on what counts as an ontology of the natural world to avoid their view slipping into this kind of ontology, but these conditions are arbitrarily imposed and once this is exposed, Super-Humeans face a dilemma. Either they can embrace Ultimate-Humeanism as the minimal ontology of the natural world compatible with physics, or (more likely) they can rethink the methodology that got them there. Overall, this paper argues that Super-Humeanism currently lacks principled motivation, outlines a framework for naturalistic ontological reductions, and exposes the consequences of unchecked adherence to a simplicity-driven methodology.

Stimulus type shapes the topology of cellular functional networks in mouse visual cortex

On the timescale of sensory processing, neuronal networks have relatively fixed anatomical connectivity, while functional interactions between neurons can vary depending on the ongoing activity of the neurons within the network. We thus hypothesized that different types of stimuli could lead those networks to display stimulus-dependent functional connectivity patterns. To test this hypothesis, we analyzed single-cell resolution electrophysiological data from the Allen Institute, with simultaneous recordings of stimulus-evoked activity from neurons across 6 different regions of mouse visual cortex. Comparing the functional connectivity patterns during different stimulus types, we made several nontrivial observations: (1) while the frequencies of different functional motifs were preserved across stimuli, the identities of the neurons within those motifs changed; (2) the degree to which functional modules are contained within a single brain region increases with stimulus complexity. Altogether, our work reveals unexpected stimulus-dependence to the way groups of neurons interact to process incoming sensory information.

Regional changes in brain metabolism during the progression of mild cognitive impairment: a longitudinal study based on radiomics

BackgroundThis study aimed to establish radiomics models based on positron emission tomography (PET) images to longitudinally predict transition from mild cognitive impairment (MCI) to Alzheimer's disease (AD).MethodsIn our study, 278 MCI patients from the ADNI database were analyzed, where 60 transitioned to AD (pMCI) and 218 remained stable (sMCI) over 48 months. Patients were divided into a training set (n = 222) and a validation set (n = 56). We first employed voxel-based analysis of 18F-FDG PET images to identify brain regions that present significant SUV difference between pMCI and sMCI groups. Radiomic features were extracted from these regions, key features were selected, and predictive models were developed for individual and combined brain regions. The models' effectiveness was evaluated using metrics like AUC to determine the most accurate predictive model for MCI progression.ResultsVoxel-based analysis revealed four brain regions implicated in the progression from MCI to AD. These include ROI1 within the Temporal lobe, ROI2 and ROI3 in the Thalamus, and ROI4 in the Limbic system. Among the predictive models developed for these individual regions, the model utilizing ROI4 demonstrated superior predictive accuracy. In the training set, the AUC for the ROI4 model was 0.803 (95% CI 0.736, 0.865), and in the validation set, it achieved an AUC of 0.733 (95% CI 0.559, 0.893). Conversely, the model based on ROI3 showed the lowest performance, with an AUC of 0.75 (95% CI 0.685, 0.809). Notably, the comprehensive model encompassing all identified regions (ROI total) outperformed the single-region models, achieving an AUC of 0.884 (95% CI 0.845, 0.921) in the training set and 0.816 (95% CI 0.705, 0.909) in the validation set, indicating significantly enhanced predictive capability for MCI progression to AD.ConclusionOur findings underscore the Limbic system as the brain region most closely associated with the progression from MCI to AD. Importantly, our study demonstrates that a PET brain radiomics model encompassing multiple brain regions (ROI total) significantly outperforms models based on single brain regions. This comprehensive approach more accurately identifies MCI patients at high risk of progressing to AD, offering valuable insights for non-invasive diagnostics and facilitating early and timely interventions in clinical settings.

Dose conformity and falloff in single-lesion intracranial SRS with DCA and VMAT methods.

Intracranial stereotactic radiosurgery (SRS) aims at achieving highly conformal dose distribution and, at the same time, attaining rapid dose falloff outside the treatment target. SRS is performed using different techniques including dynamic conformal arcs (DCA) and volumetric modulated arc therapy (VMAT). In this study, we compare dose conformity and falloff in DCA and VMAT plans for SRS with a single target. To compare dose conformity in SRS plans, we employ a novel conformity index , RTOG conformity index ( ), and Riet-Paddick conformity index ( ). In addition, we use indices , , and to evaluate dose falloff. For each of the considered 118 cases of SRS, two plans were created using DCA and VMAT. A two-tailed Student's t-test was used to evaluate the difference between the employed indices for the DCA and VMAT plans. The studied VMAT plans were characterized by higher dose conformity than the DCA plans. The differences between the conformity indices for the DCA plans and VMAT plans were statistically significant. The DCA plans had a smaller number of monitor units (MUs) and smaller indices R50%, V10 Gy, and V12 Gy than the VMAT plans. However, the differences between R50%, V10 Gy, and V12 Gy for the DCA and VMAT plans were not statistically significant. Although the studied VMAT plans had higher dose conformity, they also had larger MUs than the DCA plans. In terms of dose falloff characterized by parameters R50%, V10 Gy, and V12 Gy, DCA serves as a reasonable alternative to VMAT in the case of a single brain metastasis.

De novo versus recurrent metastatic breast cancer affects the extent of brain metastases.

We aimed to identify factors associated with the extent of brain metastases in patients with breast cancer to help distinguish brain oligometastases (1-4 brain metastases) from extensive metastases (5 or more brain metastases). This retrospective observational study included 100 female patients diagnosed with brain metastases from breast cancer at a single institution between January 2011 and April 2022. Patient demographics and tumor characteristics were compared between the brain oligometastases group and the extensive metastases group. Multivariable logistic regression analysis was performed to determine the independent factors, including age at initial diagnosis, initial stage, breast cancer subtype, detection time of brain metastases, and de novo or recurrent status of the metastatic disease. In a subgroup analysis of patients with brain oligometastases, demographic and tumor characteristics were compared between patients with single and two-four brain metastases. Of the 100 patients, 56 had brain oligometastases, while 44 had extensive brain metastases. The multivariable logistic regression analysis revealed that only the de novo/recurrent status of metastatic breast cancer was significantly associated with the extent of brain metastasis (p = 0.023). In the subgroup analysis of 56 patients with brain oligometastases, those diagnosed at an earlier stage were more likely to have a single brain metastasis (p = 0.008). Patients with de novo metastatic breast cancer are more likely to develop extensive brain metastases than those with recurrent metastatic breast cancer. This insight could influence the development of tailored approaches for monitoring and treating brain metastases, supporting the potential advantages of routine brain screening for patients newly diagnosed with stage IV breast cancer.

What is the optimal isodose line for stereotactic radiotherapy for single brain metastases using HyperArc?

The study aimed to investigate the optimal isodose line (IDL) in linear accelerator-based stereotactic radiotherapy for single brain metastasis, using HyperArc. We compared the dosimetric parameters for target and normal brain tissue among six plans with different IDLs. This study included 30 patients with single brain metastasis. We retrospectively generated six plans for each tumor with different IDLs (80%, 70%, 60%, 50%, 40%, and33%) using HyperArc. All treatment plans were normalized to the prescription dose of 35Gy in five fractions which was covered by 95% of the planning target volume (PTV), defined by adding a 1.0mm margin to the gross tumor volume (GTV). The dosimetric parameters were compared among the six plans. For GTV>0.1 cm3, the ratio of brain-GTV volumes receiving 25Gy to PTV (V25Gy/PTV) was significantly lower at IDL 40%-70% than at IDL 80% and 33% (p<0.01, retrospectively). For GTV<0.1 cm3, V25Gy/PTV decreased continuously as IDL decreased. The values of D99% and D80% for GTV increased with decreasing IDL. An IDL of 50% or less was required to achieve D99% of greater than 43Gy and D80% of greater than 50Gy. The mean values of D99% and D80% for IDL 50% were 44.3 and 51.9Gy. The optimal IDL is 40%-50% for GTV>0.1 cm3. These lower IDLs could increase D99% and D80% of GTV while lowering V25Gy of normal brain tissue, which may help reduce the risk of radiation necrosis and improve local control.

Extracting interpretable signatures of whole-brain dynamics through systematic comparison.

The brain's complex distributed dynamics are typically quantified using a limited set of manually selected statistical properties, leaving the possibility that alternative dynamical properties may outperform those reported for a given application. Here, we address this limitation by systematically comparing diverse, interpretable features of both intra-regional activity and inter-regional functional coupling from resting-state functional magnetic resonance imaging (rs-fMRI) data, demonstrating our method using case-control comparisons of four neuropsychiatric disorders. Our findings generally support the use of linear time-series analysis techniques for rs-fMRI case-control analyses, while also identifying new ways to quantify informative dynamical fMRI structures. While simple statistical representations of fMRI dynamics performed surprisingly well (e.g., properties within a single brain region), combining intra-regional properties with inter-regional coupling generally improved performance, underscoring the distributed, multifaceted changes to fMRI dynamics in neuropsychiatric disorders. The comprehensive, data-driven method introduced here enables systematic identification and interpretation of quantitative dynamical signatures of multivariate time-series data, with applicability beyond neuroimaging to diverse scientific problems involving complex time-varying systems.

Accelerated Aging after Traumatic Brain Injury: An ENIGMA Multi-Cohort Mega-Analysis.

The long-term consequences of traumatic brain injury (TBI) on brain structure remain uncertain. Given evidence that a single significant brain injury event increases the risk of dementia, brain-age estimation could provide a novel and efficient indexing of the long-term consequences of TBI. Brain-age procedures use predictive modeling to calculate brain-age scores for an individual using structural magnetic resonance imaging (MRI) data. Complicated mild, moderate, and severe TBI (cmsTBI) is associated with a higher predicted age difference (PAD), but the progression of PAD over time remains unclear. We sought to examine whether PAD increases as a function of time since injury (TSI) and if injury severity and sex interacted to influence this progression. Through the ENIGMA Adult Moderate and Severe (AMS)-TBI working group, we examine the largest TBI sample to date (n = 343), along with controls, for a total sample size of n = 540, to replicate and extend prior findings in the study of TBI brain age. Cross-sectional T1w-MRI data were aggregated across 7 cohorts, and brain age was established using a similar brain age algorithm to prior work in TBI. Findings show that PAD widens with longer TSI, and there was evidence for differences between sexes in PAD, with men showing more advanced brain age. We did not find strong evidence supporting a link between PAD and cognitive performance. This work provides evidence that changes in brain structure after cmsTBI are dynamic, with an initial period of change, followed by relative stability in brain morphometry, eventually leading to further changes in the decades after a single cmsTBI. ANN NEUROL 2024;96:365-377.

Results from METIS (EF-25), an international, multicenter phase III randomized study evaluating the efficacy and safety of tumor treating fields (TTFields) therapy in NSCLC patients with brain metastases.

2008 Background: For non-small cell lung cancer (NSCLC) with brain metastases (BM) stereotactic radiosurgery (SRS) is the current preferred therapy. Due to frequent intracranial failures, there is a high unmet need for salvage therapies. Whole brain radiotherapy (WBRT) reduces intracranial failure but used less frequently due to cognitive consequences. Tumor Treating Fields (TTFields) are electric fields that disrupt cancer cell division and have shown improved survival and safety in patients with glioblastoma and metastatic NSCLC. Phase 3 METIS trial [NCT02831959] aimed to evaluate the efficacy and safety of TTFields therapy in NSCLC patients with BM treated with SRS, specifically in terms of lengthening time to intracranial progression without cognitive decline. Methods: Mutation negative (M-) NSCLC patients with 1–10 BM were randomized 1:1 to receive stereotactic radiosurgery (SRS) followed by Tumor Treating Fields (TTFields; 150 kHz) therapy with best supportive care (BSC) or SRS followed by BSC. Patients with Karnofsky Performance Status (KPS) ≥70, newly diagnosed with one inoperable or 2–10 supra-/infratentorial brain metastases suitable for SRS and receiving optimal extracranial disease therapy were included. Exclusions were prior WBRT and single operable or recurrent brain metastases. Primary endpoint was time to first intracranial progression (RANO-BM) based on cumulative risk. Patients were followed every two months until second intracranial progression. Cognition and patient quality of life (QoL) were evaluated. Results: Between July 2016 and September 2022, 298 patients were randomized. Baseline characteristics were balanced: median age was 63.5 (range 37-84) years, 37.6% females, majority of patients had a KPS ≥ 80, median time from initial NSCLC diagnosis was 1.8 months (range: 0.2-55.7), 77% had adenocarcinoma. Median treatment duration of TTFields was 16 weeks, with a median usage time of 67%. Primary endpoint, time to intracranial progression from SRS, was significantly prolonged with SRS followed by TTFields therapy with BSC vs. TTFields plus BSC arm (median of 21.9 vs. 11.3 months); HR=0.67 [0.48-0.93], p=0.02. TTFields-related AEs were mainly dermatological, and Grade ≤2. TTFields therapy also improved deterioration-free survival of global health status, physical functioning, and fatigue according to QoL, and did not negatively impact cognition. Conclusions: METIS study met its primary endpoint, demonstrating that TTFields therapy following SRS in mutation negative NSCLC patients with BM, significantly prolongs time to intracranial progression and could postpone WBRT, without QoL and cognition decline. Clinical trial information: NCT02831959 .

Dosimetric comparison of HyperArc and InCise MLC-based CyberKnife plans in treating single and multiple brain metastases.

This study aimed to compare the dosimetric attributes of two multi-leaf collimator based techniques, HyperArc and Incise CyberKnife, in the treatment of brain metastases. 17 cases of brain metastases were selected including 6 patients of single lesion and 11 patients of multiple lesions. Treatment plans of HyperArc and CyberKnife were designed in Eclipse 15.5 and Precision 1.0, respectively, and transferred to Velocity 3.2 for comparison. HyperArc plans provided superior Conformity Index (0.91±0.06vs. 0.77±0.07, p<0.01) with reduced dose distribution in organs at risk (Dmax, p<0.05) and lower normal tissue exposure (V4Gy-V20Gy, p<0.05) in contrast to CyberKnife plans, although the Gradient Indexes were similar. CyberKnife plans showed higher Homogeneity Index (1.54±0.17vs. 1.39±0.09, p<0.05) and increased D2% and D50% in the target (p<0.05). Additionally, HyperArc plans had significantly fewer Monitor Units (MUs) and beam-on time (p<0.01). HyperArc plans demonstrated superior performance compared with MLC-based CyberKnife plans in terms of conformity and the sparing of critical organs and normal tissues, although no significant difference in GI outcomes was noted. Conversely, CyberKnife plans achieved a higher target dose and HI. The study suggests that HyperArc is more efficient and particularly suitable for treating larger lesions in brain metastases.

Recurrent cerebrovascular events after recent small subcortical infarction

BackgroundRecent small subcortical infarcts (RSSI) are the neuroimaging hallmark feature of small vessel disease (SVD)-related acute lacunar stroke. Long-term data on recurrent cerebrovascular events including their aetiology after RSSI are scarce.Patients and methodsThis retrospective study included all consecutive ischaemic stroke patients with an MRI-confirmed RSSI (in the supply area of a small single brain artery) at University Hospital Graz between 2008 and 2013. We investigated associations between clinical and SVD features on MRI (STRIVE criteria) and recurrent cerebrovascular events, using multivariable Cox regression adjusted for age, sex, vascular risk factors and MRI parameters.ResultsWe analysed 332 consecutive patients (mean age 68 years, 36% women; median follow-up time 12 years). A recurrent ischaemic cerebrovascular event occurred in 70 patients (21.1%; 54 ischaemic strokes, 22 transient ischaemic attacks) and was mainly attributed to SVD (68%). 26 patients (7.8%) developed intracranial haemorrhage. In multivariable analysis, diabetes (HR 2.43, 95% CI 1.44–3.88), severe white matter hyperintensities (HR 1.97, 95% CI 1.14–3.41), and cerebral microbleeds (HR 1.89, 95% CI 1.32–3.14) on baseline MRI were related to recurrent ischaemic stroke/TIA, while presence of cerebral microbleeds increased the risk for intracranial haemorrhage (HR 3.25, 95% CI 1.39–7.59). A widely used SVD summary score indicated high risks of recurrent ischaemic (HR 1.22, 95% CI 1.01–1.49) and haemorrhagic cerebrovascular events (HR 1.57, 95% CI 1.11–2.22).ConclusionPatients with RSSI have a substantial risk for recurrent cerebrovascular events—particularly those with coexisting chronic SVD features. Recurrent events are mainly related to SVD again.

MSCLK: Multi-scale fully separable convolution neural network with large kernels for early diagnosis of Alzheimer’s disease

Alzheimer’s disease (AD) is identified as a central nervous system disease that exhibits irreversible degeneration, while mild cognitive impairment (MCI) is viewed as the preliminary stage of AD, and its pathogenesis is notably intricate. MCI contains two stages: early MCI (EMCI), and late MCI (LMCI). EMCI diagnosis can prevent EMCI from progressing to LMCI, and then to AD. Therefore, accurate diagnosis of EMCI/LMCI is crucial for developing the early intervention and treatment strategies of AD. Currently, most existing EMCI/LMCI diagnostic methods use single modality images, while different modality images carry different complementary information that helps for accurate diagnosis of EMCI/LMCI, and the lesion area is usually not limited to a single brain area, which involves multiple regions. In this case, conventional convolution operations cannot be able to accurately extract the pathological features of AD. In this work, we propose a novel Multi-scale fully Separable Convolution neural network with Large Kernels (MSCLK) method to diagnose early Alzheimer’s disease with structural Magnetic Resonance Imaging (sMRI) images. MSCLK mainly consists of the multi-scale 3D fully separable convolution modules and the deep metric learning module. The multi-scale convolution that contains both small and large kernels is used to effectively capture the discrimination features of different scale acceptance domains. 3D fully separable convolution is used to reduce parameters and overfitting. The deep metric learning is used to learn hard samples that are similar but belong to different classes. We also propose a variant method of MSCLK (called MSCLK-Fusion MRI and PET, MSCLK-FMP) by adding the pixel-level fusion module and feature-level fusion module into the MSCLK framework to integrate the sMRI image and the Positron Emission Computed Tomography (PET) image for further improving the accuracy of EMCI vs. LMCI classification task. The pixel-level fusion is used to achieve early pixel-level fusion of sMRI and PET images, and the feature-level fusion is used to achieve high-dimensional feature-level fusion of sMRI and PET images. Experimental results on the ADNI database show that the performance of our MSCLK and MSCLK-FMP are superior to other state-of-the-art methods. The accuracy of MSCLK achieves 98.89%, 95.97%, 96.39% and 98.76% for AD vs. EMCI, AD vs. LMCI, EMCI vs. NC and LMCI vs. NC classification tasks, respectively, and MSCLK-FMP achieves 93.93% for EMCI vs. LMCI classification task, indicating that MSCLK/MSCLK-FMP can be effectively used for diagnosing MCI patients. Moreover, our MSCLK-FMP is capable of pinpointing key brain areas involved in the pathological progression of MCI, such as the Temporal_Inf, the Hippocampus, the Precuneus, the Precentral, and the Thalamus. These findings contribute to uncovering the early onset of AD pathogenesis.

Holoprosencephaly: The Disease and Its Related Disabilities

AbstractHoloprosencephaly (HPE), the most prevalent developmental anomaly affecting the forebrain in humans, occurs in ∼1 in 16,000 liveborn neonates, with an incidence reaching 1 in 250 in conceptuses. This condition is distributed worldwide. HPE is etiologically heterogeneous, and its pathogenesis is variable. Environmental, teratogenic, genetic, or metabolic factors can contribute to the development of HPE. Notably, maternal insulin-dependent diabetes mellitus and maternal alcoholism are among the primary causative factors. HPE may be linked to various well-defined multiple malformation syndromes characterized by a normal karyotype, such as Smith–Lemli–Opitz's, Pallister–Hall's, or velocardiofacial syndrome. Alternatively, it can be associated with chromosomal abnormalities. (i.e., Patau's syndrome and, less frequently, Edwards' syndrome or Down's syndrome). The major genes implicated in HPE are SHH, ZIC2, SIX3, and TGIF. The range of HPE is extensive, covering diverse neuropathological phenotypes of varying severity. Three classical types of HPE can be distinguished in increasing order of severity: lobar HPE, characterized by separated right and left ventricles with some continuity across the frontal cortex; semilobar HPE, featuring a partial separation; and the most severe form, alobar HPE, where there is a single brain ventricle and the absence of an interhemispheric fissure. Additionally, there are other variations of HPE, ranging in severity, including the less severe interhemispheric median HPE (also known as middle interhemispheric variant). The phenotypic spectrum of HPE is highly extensive, encompassing severe cerebral malformations to microforms. Children with HPE often encounter numerous medical challenges; among them neurological disorders, craniofacial malformations, endocrine disorders, oral and motor dysfunction, and dysfunction of the autonomic nervous system. Neurologic problems, such as cerebral palsy and seizures, are common. The diagnosis of HPE is typically made prenatally, relying primarily on ultrasound and magnetic resonance imaging examinations. The prognosis for individuals with HPE is largely dependent on its underlying causes. Those with cytogenetic abnormalities, in particular, face a significantly poorer prognosis, with only 2% surviving beyond 1 year.

Spectral Graph Neural Network-Based Multi-Atlas Brain Network Fusion for Major Depressive Disorder Diagnosis.

Major Depressive Disorder (MDD) imposes a substantial burden within the healthcare domain, impacting millions of individuals worldwide. Functional Magnetic Resonance Imaging (fMRI) has emerged as a promising tool for the objective diagnosis of MDD, enabling the investigation of functional connectivity patterns in the brain associated with this disorder. However, most existing methods focus on a single brain atlas, which limits their ability to capture the complex, multi-scale nature of functional brain networks. To address these limitations, we propose a novel multi-atlas fusion method that incorporates early and late fusion in a unified framework. Our method introduces the concept of the holistic Functional Connectivity Network (FCN), which captures both intra-atlas relationships within individual atlases and inter-regional relationships between atlases with different brain parcellation scales. This comprehensive representation enables the identification of potential disease-related patterns associated with MDD in the early stage of our framework. Moreover, by decoding the holistic FCN from various perspectives through multiple spectral Graph Convolutional Neural Networks and fusing their results with decision-level ensembles, we further improve the performance of MDD diagnosis. Our approach is easily implemented with minimal modifications to existing model structures and demonstrates a robust performance across different baseline models. Our method, evaluated on public resting-state fMRI datasets, surpasses the current multi-atlas fusion methods, enhancing the accuracy of MDD diagnosis. The proposed novel multi-atlas fusion framework provides a more reliable MDD diagnostic technique. Experimental results show our approach outperforms both single- and multi-atlas-based methods, demonstrating its effectiveness in advancing MDD diagnosis.

Early Blood-Brain Barrier Impairment as a Pathological Hallmark in a Novel Model of Closed-Head Concussive Brain Injury (CBI) in Mice.

Concussion, caused by a rotational acceleration/deceleration injury mild enough to avoid structural brain damage, is insufficiently captured in recent preclinical models, hampering the relation of pathophysiological findings on the cellular level to functional and behavioral deficits. We here describe a novel model of unrestrained, single vs. repetitive concussive brain injury (CBI) in male C56Bl/6j mice. Longitudinal behavioral assessments were conducted for up to seven days afterward, alongside the evaluation of structural cerebral integrity by in vivo magnetic resonance imaging (MRI, 9.4 T), and validated ex vivo by histology. Blood-brain barrier (BBB) integrity was analyzed by means of fluorescent dextran- as well as immunoglobulin G (IgG) extravasation, and neuroinflammatory processes were characterized both in vivo by positron emission tomography (PET) using [18F]DPA-714 and ex vivo using immunohistochemistry. While a single CBI resulted in a defined, subacute neuropsychiatric phenotype, longitudinal cognitive testing revealed a marked decrease in spatial cognition, most pronounced in mice subjected to CBI at high frequency (every 48 h). Functional deficits were correlated to a parallel disruption of the BBB, (R2 = 0.29, p < 0.01), even detectable by a significant increase in hippocampal uptake of [18F]DPA-714, which was not due to activation of microglia, as confirmed immunohistochemically. Featuring a mild but widespread disruption of the BBB without evidence of macroscopic damage, this model induces a characteristic neuro-psychiatric phenotype that correlates to the degree of BBB disruption. Based on these findings, the BBB may function as both a biomarker of CBI severity and as a potential treatment target to improve recovery from concussion.

Validation of a computational biomechanical mouse brain model for rotational head acceleration.

Recent mouse brain injury experiments examine diffuse axonal injury resulting from accelerative head rotations. Evaluating brain deformation during these events would provide valuable information on tissue level thresholds for brain injury, but there are many challenges to imaging the brain's mechanical response during dynamic loading events, such as a blunt head impact. To address this shortcoming, we present an experimentally validated computational biomechanics model of the mouse brain that predicts tissue deformation, given the motion of the mouse head during laboratory experiments. First, we developed a finite element model of the mouse brain that computes tissue strains, given the same head rotations as previously conducted in situ hemicephalic mouse brain experiments. Second, we calibrated the model using a single brain segment, and then validated the model based on the spatial and temporal strain responses of other regions. The result is a computational tool that will provide researchers with the ability to predict brain tissue strains that occur during mouse laboratory experiments, and to link the experiments to the resulting neuropathology, such as diffuse axonal injury.

Short-term predictors of stereotactic radiosurgery outcome for untreated single non-small cell lung cancer brain metastases: a restrospective cohort study.

Stereotactic radiosurgery (SRS) is an option for brain metastases (BM) not eligible for surgical resection, however, predictors of SRS outcomes are poorly known. The aim of this study is to investigate predictors of SRS outcome in patients with BM secondary to non-small cell lung cancer (NSCLC). The secondary objective is to analyze the value of volumetric criteria in identifying BM progression. This retrospective cohort study included patients >18 years of age with a single untreated BM secondary to NSCLC. Demographic, clinical, and radiological data were assessed. The primary outcome was treatment failure, defined as a BM volumetric increase 12 months after SRS. The unidimensional measurement of the BM at follow-up was also assessed. One hundred thirty-five patients were included, with a median BM volume at baseline of 1.1 cm3 (IQR 0.4-2.3). Fifty-two (38.5%) patients had SRS failure at follow-up. Only right BM laterality was associated with SRS failure (p=0.039). Using the volumetric definition of SRS failure, the unidimensional criteria demonstrated a sensibility of 60.78% (46.11%-74.16%), specificity of 89.02% (80.18%-94.86%), positive LR of 5.54 (2.88-10.66) and negative LR of 0.44 (0.31-0.63). SRS demonstrated a 61.5% local control rate 12 months after treatment. Among the potential predictors of treatment outcome analyzed, only the right BM laterality had a significant association with SRS failure. The volumetric criteria were able to identify more subtle signs of BM increase than the unidimensional criteria, which may allow earlier diagnosis of disease progression and use of appropriate therapies.