Brain structural differences in cocaine use disorder: Insights from multivariate and neurotransmitter analyses.

Cocaine use disorder (CUD) is a chronic and relapsing neuropsychiatric disorder characterized by structural and functional brain lesions, posing a significant public health challenge. While the disruptive effects of cocaine on neurotransmitter systems (receptors/transporters) have been well established, the patterns of brain structural abnormalities in CUD and its interaction with other factors remain an ongoing topic of investigation. We employed source-based morphometry (SBM), a multivariate approach on 50 CUD participants and 50 matched healthy controls from the public SUDMEX CONN dataset. This method allowed us to identify co-varying patterns of brain tissue volume differences, and further explore the effect of average cocaine dosage through moderation analysis. Spatial correlation analysis was also performed to examine micro-macro structural consistency between tissue volume variations and chemoarchitectural distribution of dopamine and serotonin. Our SBM analysis findings were consistent with reward-related neuroadaptations in the striato-thalamo-cortical and limbic pathways and also exhibited co-localization with the distribution of dopamine and serotonin systems. The moderation analysis suggested that the average dosage positively strengthens cocaine consumption years' effect on brain structures. By integrating our findings of gray and white matter volume differences and corresponding neurotransmitter profiles, this comprehensive view not only strengthens our understanding of the brain's structural abnormalities in CUD, but also reveals potential mechanisms underlying its development and progression.

Combining Quantitative Susceptibility Mapping With the Gray Matter Volume to Predict Neurological Deficits in Patients With Small Artery Occlusion.

BACKGROUND: Currently, there is still a lack of valuable neuroimaging markers to assess the clinical severity of stroke patients with small artery occlusion (SAO). Quantitative susceptibility mapping (QSM) is a quantitative processing method for neuroradiological diagnostics. Gray matter (GM) volume changes in stroke patients are also proved to be associated with neurological deficits. This study aims to explore the predictive value of QSM and GM volume in neurological deficits of patients with SAO. METHODS: As neurological deficits, the National Institutes of Health Stroke Scale (NIHSS) was used. Sixty-six SAO participants within 24 h of first onset were enrolled and divided into mild and moderate groups based on NIHSS. QSM values of infarct area and GM volume were calculated from magnetic resonance imaging (MRI) data. Two-sample t-tests were used to compare differences in QSM value and GM volume between the two groups, and the diagnostic efficacy of the combination of QSM value and GM volume was evaluated. RESULTS: The results revealed both the QSM value and GM volume within the infarct area of the moderate group were lower compared to the mild group. Moderate group exhibited lower GM volume in some specific gyrus compared with mild group in the case of voxel-wise GM volume on whole-brain voxel level. The support vector machine (SVM) classifier's analysis showed a high power for the combination of QSM value, GM volume within the infarct area, and voxel-wise GM volume. CONCLUSION: Our research first reported the combination of QSM value, GM volume within the infarct area, and voxel-wise GM volume could be used to predict neurological impairment of patients with SAO, which provides new insights for further understanding the SAO stroke.

Corrigendum: Diagnosis for autism spectrum disorder children using T1-based gray matter and arterial spin labeling-based cerebral blood flow network metrics.

[This corrects the article DOI: 10.3389/fnins.2024.1356241.].

Pain resilience dimensions and regional gray matter volume as risk factors for poor outcomes of chronic pain: a prospective cohort study.

BACKGROUND: Pain resilience and regional gray matter volume (rGMV) are established correlates of adaptation to chronic pain within cross-sectional studies. Extending such work, this prospective cohort study tested the status of baseline pain resilience dimension scores and rGMV as risk factors for subsequent exacerbations in chronic pain disability and intensity. METHODS: 142 adults with chronic musculoskeletal pain completed an initial assessment comprising a structural magnetic resonance imaging scan and self-report measures of cognitive/affective positivity and behavioral perseverance pain resilience dimensions, disability, pain intensity, and demographics. Disability and pain intensity were outcomes re-assessed at a 6-month follow-up. The impact of pain resilience dimension scores and identified rGMV sites on follow-up outcomes was examined after controlling for other baseline correlates of outcomes. Mediating effects of identified rGMV sites on pain resilience dimension-follow-up outcome relations were also evaluated. RESULTS: Aside from the significant multivariate effect of lower behavioral perseverance and cognitive/affective positivity scores, augmented left precuneus, temporal pole, superior temporal gyrus (STG), and precentral gyrus rGMV combined to predict higher follow-up disability levels, independent of covariates. Higher left fusiform gyrus rGMV levels predicted follow-up exacerbations in pain intensity, but pain resilience dimension scores did not. Finally, left precuneus and left temporal pole STG rGMV partially mediated cognitive/affective positivity-follow-up disability relations. CONCLUSIONS: Findings underscore deficits in pain resilience and increased rGMV as potential risk factors for poorer subsequent outcomes of chronic musculoskeletal pain and provide foundations for further prospective extensions as well as targeted intervention research.

Gray Matter Volume Correlates of Co-Occurring Depression in Autism Spectrum Disorder.

Autism Spectrum Disorder (ASD) involves neurodevelopmental syndromes with significant deficits in communication, motor behaviors, emotional and social comprehension. Often, individuals with ASD exhibit co-occurring depression characterized by a change in mood and diminished interest in previously enjoyable activities. Due to communicative challenges and a lack of appropriate assessments in this cohort, co-occurring depression can often go undiagnosed during routine clinical examinations and, thus, its management neglected. The literature on co-occurring depression in adults with ASD is limited. Therefore, understanding the neural basis of the co-occurring psychopathology of depression in ASD is crucial for identifying brain-based markers for its timely and effective management. Using structural MRI and phenotypic data from the Autism Brain Imaging Data Exchange (ABIDE II) repository, we examined the pattern of relationship regional grey matter volume (rGMV) has with co-occurring depression and autism severity within regions of a priori interest in adults with ASD (n = 44; age = 17-28 years). Further, we performed an exploratory analysis of the rGMV differences between ASD and matched typically developed (TD, n = 39; age = 18-31 years) samples. The severity of co-occurring depression correlated negatively with the rGMV of the right thalamus. Additionally, a significant interaction was evident between the severity of co-occurring depression and core ASD symptoms towards explaining the rGMV in the left cerebellum crus II. The results further the understanding of the neurobiological underpinnings of co-occurring depression in adults with ASD towards exploring neuroimaging-based biomarkers in the same cohort.

Reproducibility of 3D chemical exchange saturation transfer (CEST) contrasts in the healthy brain at 3T.

Chemical exchange saturation transfer (CEST) imaging may provide novel contrast for the diagnosis, prognosis, and monitoring of the progression or treatment of neurological applications. However, the reproducibility of prominent CEST contrasts like amide CEST and nuclear Overhauser enhancement (NOE) CEST must be characterized in healthy brain gray matter (GM) and white matter (GM) prior to clinical implementation. The objective of this study was to characterize the reproducibility of four different CEST contrasts in the healthy human brain. Using a 3T MRI scanner, two 3D CEST scans were acquired in 12 healthy subjects (7 females, mean age (± SD) 26 ± 4 years) approximately 10 days apart. Scan-rescan reproducibility was measured for four contrasts: amine/amide concentration-independent detection (AACID), Amide*, and inverse magnetization transfer ratio (MTRRex) contrast for amide and NOE. Reproducibility was evaluated between- and within-subjects using coefficients of variation (CV) and the percent difference between measurements. AACID and NOE-MTRRex contrasts demonstrated the lowest within-subject CVs (0.8-1.2% and 1.6-2.0%, respectively), between-subject CVs (1.2-2.1% and 3.4-4.2%, respectively), and percent difference (1.2-1.4% and 2.2-2.8%, respectively) for both GM and WM. AACID and NOE-MTRRex contrasts demonstrated the highest reproducibility and represented stable measurements suitable for characterizing changes in brain tissue caused by pathological processes.

Assessing Language Lateralization through Gray Matter Volume: Implications for Preoperative Planning in Brain Tumor Surgery.

BACKGROUND/OBJECTIVES: Functional MRI (fMRI) is widely used to assess language lateralization, but its application in patients with brain tumors can be hindered by cognitive impairments, compensatory neuroplasticity, and artifacts due to patient movement or severe aphasia. Gray matter volume (GMV) analysis via voxel-based morphometry (VBM) in language-related brain regions may offer a stable complementary approach. This study investigates the relationship between GMV and fMRI-derived language lateralization in healthy individuals and patients with left-hemisphere brain tumors, aiming to enhance accuracy in complex cases. METHODS: The MRI data from 22 healthy participants and 28 individuals with left-hemisphere brain tumors were analyzed. Structural T1-weighted and functional images were obtained during three language tasks. Language lateralization was assessed based on activation in predefined regions of interest (ROIs), categorized as typical (left) or atypical (right or bilateral). The GMV in these ROIs was measured using VBM. Linear regressions explored GMV-lateralization associations, and logistic regressions predicted the lateralization based on the GMV. RESULTS: In the healthy participants, typical left-hemispheric language dominance correlated with higher GMV in the left pars opercularis of the inferior frontal gyrus. The brain tumor participants with atypical lateralization showed increased GMV in six right-hemisphere ROIs. The GMV in the language ROIs predicted the fMRI language lateralization, with AUCs from 80.1% to 94.2% in the healthy participants and 78.3% to 92.6% in the tumor patients. CONCLUSIONS: GMV analysis in language-related ROIs effectively complements fMRI for assessing language dominance, particularly when fMRI is challenging. It correlates with language lateralization in both healthy individuals and brain tumor patients, highlighting its potential in preoperative language mapping. Further research with larger samples is needed to refine its clinical utility.

Highfield imaging of the subgenual anterior cingulate cortex in uni- and bipolar depression.

Background: The subgenual Anterior Cingulate Cortex (sgACC), as a part of the Anterior Cingulate Cortex and the limbic system plays a crucial role in mood regulation. Previous structural and functional brain imaging studies of the sgACC have revealed alterations of Gray Matter (GM) volumes and Blood Oxygenation Level Dependent signals (BOLD) in patients with Major Depressive Disorder (MDD) and Bipolar Disorder (BD), suggesting potential biomarker traits for affective disorders. Method: In this study we investigated the gray matter volume of the sgACC in 3 different patient groups: 40 MDD patients, of which 20 were medicated (MDDm) and 20 were unmedicated (MDDu), and 21 medicated BD patients, and compared them with 23 healthy volunteers. We examined GM volume alteration using high-resolution 7T Magnetic Resonance Imaging (MRI) which produced quantitative maps of the spin-lattice relaxation time (T1). T1 maps provide high contrast between gray and white matter, and at 7 Tesla voxels with submillimeter resolution can be acquired in a reasonable scan time. We developed a semi-automatic segmentation protocol based on refined landmarks derived from previous volumetric studies using quantitative T1 maps as raw input data for automatic tissue segmentation of GM, WM and CSF (cerebrospinal fluid) tissue. The sgACC ROI was then superimposed on these tissue probability maps and traced manually by two independent raters (F.B., M.M.) following our semi-automatic segmentation protocol. Interrater reliability was calculated on a subset of 10 brain scans for each hemisphere, showing an Intra-Class Correlation coefficient (ICC) r = 0.96 for left sgACC and r = 0.84 for right sgACC respectively. In summary, we have developed a reproducible and reliable semi-automatic segmentation protocol to measure gray matter volume in the sgACC. Based on previous findings from meta-analyses on morphometric studies of the sgACC, we hypothesized that patients with MDD would have lower gray matter sgACC volumes compared to healthy subjects. Results: Post-hoc analysis revealed smaller subgenual volumes for the left hemisphere in both the medicated (MDDm) and non-medicated (MDDu) group versus healthy controls (p = .001, p = .008) respectively. For the right hemisphere, the (MDDu) and BD group exhibited significantly lower subgenual volumes than healthy controls (p < .001, p = .004) respectively. Conclusion: To our knowledge, this is the first morphometric MRI study using T1 maps obtained in high-resolution 7 Tesla MRI to compare MDD and BD patients with healthy controls.

Neurostructural changes in schizophrenia and treatment-resistance: a narrative review.

Schizophrenia is a complex disorder characterized by multiple neurochemical abnormalities and structural changes in the brain. These abnormalities may begin before recognizable clinical symptoms appear and continue as a dynamic process throughout the illness. Recent advances in imaging techniques have significantly enriched our comprehension of these structural alterations, particularly focusing on gray and white matter irregularities and prefrontal, temporal, and cingulate cortex alterations. Some of the changes suggest treatment resistance to antipsychotic medications, while treatment nonadherence and relapses may further exacerbate structural abnormalities. This narrative review aims to discuss the literature about alterations and deficits within the brain, which could improve the understanding of schizophrenia and how to interpret neurostructural changes.

Correlations of gray matter volume with peripheral cytokines in Parkinson's disease.

INTRODUCTION: Peripheral cytokine levels may affect specific brain volumes. Few studies have examined this possible relationship. OBJECTIVE: In a case-control study, we used magnetic resonance imaging (MRI) voxel-based morphological analysis techniques to examine the relationship between gray matter volume changes and cognitive, motor and emotional dysfunction as well as between gray matter volume changes and peripheral blood cytokine levels. METHOD: A total of 134 subjects, comprising 66 PD patients and 68 healthy controls, were recruited. Peripheral venous blood was collected to measure the concentrations of 12 cytokines, including IL-1ß, IL-2, IL-4, IL-5, IL-6, IL-8, IL-10, IL-12p70, IL-17, IFN-α, IFN-γ, and TNF-α. All the subjects also underwent MRI, where 3D-T1-weighted MR images were used for the analysis. In addition, the Montreal Cognitive Assessment (MoCA), Mini-Mental Status Examination (MMSE), Unified Parkinson's disease Rating Scale (UPDRS), Hamilton Anxiety Scale (HAMA), and Hamilton Depression Scale (HAMD) scores were assessed in PD patients. Statistical parameter mapping 12 software was used for the statistical analysis of the images. RESULT: Compared with control patients, PD patients presented decreased gray matter volume (GMV) in the bilateral frontal lobe, temporal lobe, parietal lobe, occipital lobe, insula, and right cerebellar lobule VIII. Regional GMV in the temporal lobe, parietal lobe, and cerebellum was correlated with MoCA, MMSE, UPDRS, HAMA, and HAMD scores in PDs. In addition, the regional GMV in PDs was correlated with the concentrations of cytokines, including IL-4, IL-6, IFN-γ, and TNF-α. The IL-6 concentration was negatively correlated with the UPDRS-IV score. CONCLUSION: PD patients exhibit gray matter atrophy in a wide range of brain regions, which are symmetrically distributed and mainly concentrated in the frontal and temporal lobes, and these changes may be linked to motor disorders and neuropsychiatric manifestations. Cytokine concentrations in peripheral blood are correlated with regional gray matter volume in PDs, and the IL-6 level affects gray matter volume in the left precentral gyrus and the manifestation of motor complications.

MRI radiomics combined with machine learning for diagnosing mild cognitive impairment: a focus on the cerebellar gray and white matter.

Objective: Mild Cognitive Impairment (MCI) is a recognized precursor to Alzheimer's Disease (AD), presenting a significant risk of progression. Early detection and intervention in MCI can potentially slow disease advancement, offering substantial clinical benefits. This study employed radiomics and machine learning methodologies to distinguish between MCI and Normal Cognition (NC) groups. Methods: The study included 172 MCI patients and 183 healthy controls from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, all of whom had 3D-T1 weighted MRI structural images. The cerebellar gray and white matter were segmented automatically using volBrain software, and radiomic features were extracted and screened through Pyradiomics. The screened features were then input into various machine learning models, including Random Forest (RF), Logistic Regression (LR), eXtreme Gradient Boosting (XGBoost), Support Vector Machines (SVM), K Nearest Neighbors (KNN), Extra Trees, Light Gradient Boosting Machine (LightGBM), and Multilayer Perceptron (MLP). Each model was optimized for penalty parameters through 5-fold cross-validation to construct radiomic models. The DeLong test was used to evaluate the performance of different models. Results: The LightGBM model, which utilizes a combination of cerebellar gray and white matter features (comprising eight gray matter and eight white matter features), emerges as the most effective model for radiomics feature analysis. The model demonstrates an Area Under the Curve (AUC) of 0.863 for the training set and 0.776 for the test set. Conclusion: Radiomic features based on the cerebellar gray and white matter, combined with machine learning, can objectively diagnose MCI, which provides significant clinical value for assisted diagnosis.

Structural lesions and transcriptomic specializations shape gradient perturbations in Wilson disease.

Functional dysregulations in multiple regions are caused by excessive copper deposition in the brain in Wilson disease (WD) patients. The genetic mechanism of WD is thought to involve the abnormal expression of ATP7B in the liver, whereas the biological and molecular processes involved in functional dysregulation within the brain remain unexplored. The objective of this study was to unravel the underpinnings of functional gradient perturbations underlying structural lesions and transcriptomic specializations in WD. In this study, we included 105 WD patients and 93 healthy controls who underwent structural and functional MRI assessments. We used the diffusion mapping embedding model to derive the functional connectome gradient and further employed gray matter volume to uncover structure-function decoupling for WD. Then, we used Neurosynth, clinical data, and whole-brain gene expression data to examine the meta-analytic cognitive function, clinical phenotypes, and transcriptomic specializations related to WD gradient alterations. Compared with controls, WD patients exhibited global topographic changes in the principal pramary-to-transmodal gradient. Meta-analytic terms and clinical characteristics were correlated with these gradient alterations in motor-related processing, higher-order cognition, neurological symptoms, and age. Spatial correlations revealed structure-function decoupling in multiple networks, especially in subcortical and visual networks. Within the cortex, the spatial association between gradient alterations and gene expression profiles has revealed transcriptomic specilizations in WD that display properties indicative of ion homeostasis, neural development, and motor control. Furthermore, for the first time, we characterized the role of the ATP7B gene in impacting subcortical function. The transcriptomic specializations of WD were also associated with other neurological and psychiatric disorders. Finally, we revealed that structural lesions and gradient perturbations may share similar transcriptomic specializations in WD. In conclusion, these findings bridged functional gradient perturbations to structural lesions and gene expression profiles in WD patients, possibly promoting our understanding of the neurobiological mechanisms underlying the emergence of complex neurological and psychiatric phenotypes.

Parietal memory network and memory encoding versus retrieval impairments in PD-MCI patients: A hippocampal volume and cortical thickness study.

OBJECTIVE: The pathophysiology behind memory impairment in Parkinson's Disease Mild Cognitive Impairment (PD-MCI) is unclear. This study aims to investigate the hippocampal and cortical atrophy patterns in PD-MCI patients with different types of memory impairments, categorized as Retrieval Failure (RF) and Encoding Failure (EF). METHODS: The study included 16 healthy controls (HC) and 34 PD-MCI patients, divided into RF (N = 18) and EF (N = 16) groups based on their Verbal Memory Processes Test (VMPT) scores, including spontaneous recall, recognition, and Index of Sensitivity to Cueing (ISC). Hippocampal subfields and cortical thicknesses were measured using the FreeSurfer software for automatic segmentation. RESULTS: Compared to the HC group, the EF group exhibited significant atrophy in the left lateral occipital region and the right caudal middle frontal, superior temporal, and inferior temporal regions (p⟨0.05). The RF group displayed significant atrophy in the left lateral occipital, middle temporal, and precentral regions, as well as the right pars orbitalis and superior frontal regions (p⟨0.05). Hippocampal subfield analysis revealed distinct volume differences between HC-EF and RF-EF groups, with significant reductions in the CA1, CA3, and CA4 subregions in the EF group, but no differences between HC and RF groups (p > 0.05). CONCLUSION: Gray matter atrophy patterns differ in PD-MCI patients with encoding and retrieval memory impairments. The significant hippocampal atrophy in the EF group, particularly in the CA subregions, highlights its potential role in disease progression and memory decline. Additionally, the convergence of atrophy in the lateral occipital cortex across both RF and EF groups suggests the involvement of the Parietal Memory Network (PMN) in PD-related memory impairment.

Gray matter and cognitive alteration related to chronic obstructive pulmonary disease patients: combining ALE meta-analysis and MACM analysis.

Chronic obstructive pulmonary disease (COPD) is frequently comorbid with cognitive impairment, but it has not been paid enough attention, and its neuroanatomical characteristics have not been fully identified. Voxel-based morphometric (VBM) studies comparing gray matter (GM) abnormalities in COPD patients with healthy controls (HCs) were searched using 8 electronic databases from the inception to March 2023. Stereotactic data were extracted and tested for convergence and differences using the activation likelihood estimation (ALE) method. Moreover, based on the ALE results, a structural meta-analytic connectivity modeling (MACM) was conducted to explore the co-atrophy pattern in patients with COPD. Last, behavioral analysis was performed to assess the functional roles of the regions affected by COPD. In total, 11 studies on COPD with 949 participants were included. Voxel-based meta-analysis revealed significant GM abnormalities in the right postcentral gyrus (including inferior parietal lobule), left precentral gyrus, and left cingulate gyrus (including paracentral lobule) in patients with COPD compared with HCs. Further MACM analysis revealed a deeper co-atrophy pattern between the brain regions with abnormal GM structure and the insula in COPD patients. Behavioral analysis showed that the abnormal GM structure in the left cingulate gyrus (including paracentral lobule) was strongly associated with cognitive function, especially executive function. COPD comorbid with cognitive impairment has a specific neurostructural basis of GM structural abnormalities, which may also involve a deeper co-atrophy pattern between the insula. These findings enhance our understanding of the underlying neuropathogenesis and suggest potential imaging markers for cognitive impairment in COPD patients. PROSPERO registration number: CRD42022298722.

Gray matter volumetric changes in tinnitus: The impact of hearing loss and severity.

Tinnitus is a phantom auditory sensation that commonly co-occurs with hearing loss. Both tinnitus and hearing loss can impact the quality of life, emotional well-being, and cognitive functioning of the affected individuals. While previous studies have highlighted structural alterations in hearing loss and/or tinnitus, the fundamental neural mechanisms underpinning tinnitus severity remain poorly understood. In this study, we conducted a voxel-based morphometry to investigate gray matter (GM) volume differences among groups of participants with varying tinnitus severity and hearing status, and controls within a large sample. We observed reduced GM volume in the left anterior insula and right planum polare in participants with hearing loss, regardless of their tinnitus status, compared to normal hearing controls. We noted decreased GM volume in the bilateral anterior and posterior insula for those with tinnitus and normal hearing compared to a normal hearing control group. Further, the tinnitus with hearing loss group showed decreased GM volume in the left planum polare, left inferior temporal gyrus, bilateral anterior temporal gyri, and right superior frontal gyrus compared to the normal hearing control group, suggesting a combined effect of hearing loss and tinnitus. While tinnitus severity did not show a significant overall effect, there was a significant positive correlation between tinnitus distress and GM volume in bilateral planum polare. Our findings enhance the understanding of structural brain changes related to hearing loss and tinnitus, and advance the overall knowledge of tinnitus pathophysiology, which can contribute to the development of more effective treatments for tinnitus.

Disrupted gray matter connectome in vestibular migraine: a combined machine learning and individual-level morphological brain network analysis.

BACKGROUND: Although gray matter (GM) volume alterations have been extensively documented in previous voxel-based morphometry studies on vestibular migraine (VM), little is known about the impact of this disease on the topological organization of GM morphological networks. This study investigated the altered network patterns of the GM connectome in patients with VM. METHODS: In this study, 55 patients with VM and 57 healthy controls (HCs) underwent structural T1-weighted MRI. GM morphological networks were constructed by estimating interregional similarity in the distributions of regional GM volume based on the Kullback-Leibler divergence measure. Graph-theoretical metrics and interregional morphological connectivity were computed and compared between the two groups. Partial correlation analyses were performed between significant GM connectome features and clinical parameters. Logistic regression (LR), support vector machine (SVM), and random forest (RF) classifiers were used to examine the performance of significant GM connectome features in distinguishing patients with VM from HCs. RESULTS: Compared with HCs, patients with VM exhibited increased clustering coefficient and local efficiency, as well as reduced nodal degree and nodal efficiency in the left superior temporal gyrus (STG). Furthermore, we identified one connected component with decreased morphological connectivity strength, and the involved regions were mainly located in the STG, temporal pole, prefrontal cortex, supplementary motor area, cingulum, fusiform gyrus, and cerebellum. In the VM group, several connections in the identified connected component were correlated with clinical measures (i.e., symptoms and emotional scales); however, these correlations did not survive multiple comparison corrections. A combination of significant graph- and connectivity-based features allowed single-subject classification of VM versus HC with significant accuracy of 77.68%, 77.68%, and 72.32% for the LR, SVM, and RF models, respectively. CONCLUSION: Patients with VM had aberrant GM connectomes in terms of topological properties and network connections, reflecting potential dizziness, pain, and emotional dysfunctions. The identified features could serve as individualized neuroimaging markers of VM.

Specialized gray matter segmentation via a generative adversarial network: application on brain white matter hyperintensities classification.

Background: White matter hyperintensities (WMH) observed in T2 fluid-attenuated inversion recovery (FLAIR) images have emerged as potential markers of neurodegenerative diseases like Multiple Sclerosis (MS). Lacking comprehensive automated WMH classification systems in current research, there is a need to develop accurate detection and classification methods for WMH that will benefit the diagnosis and monitoring of brain diseases. Objective: Juxtacortical WMH (JCWMH) is a less explored subtype of WMH, primarily due to the hard definition of the cortex in FLAIR images, which is escalated by the presence of lesions to obtain appropriate gray matter (GM) masks. Methods: In this study, we present a method to perform a specialized GM segmentation developed for the classification of WMH, especially JCWMH. Using T1 and FLAIR images, we propose a pipeline to integrate masks of white matter, cerebrospinal fluid, ventricles, and WMH to create a unique mask to refine the primary GM map. Subsequently, we utilize this pipeline to generate paired data for training a conditional generative adversarial network (cGAN) to substitute the pipeline and reduce the inputs to only FLAIR images. The classification of WMH is then based on the distances between WMH and ventricular and GM masks. Due to the lack of multi-class labeled WMH datasets and the need for extensive data for training deep learning models, we attempted to collect a large local dataset and manually segment and label some data for WMH and ventricles. Results: In JCWMH classification, the proposed method exhibited a Dice similarity coefficient, precision, and sensitivity of 0.76, 0.69, and 0.84, respectively. With values of 0.66, 0.55, and 0.81, the proposed method clearly outperformed the approach commonly used in the literature, which uses extracted GM masks from registered T1 images on FLAIR. Conclusion: After training, the method proves its efficiency by providing results in less than one second. In contrast, the usual approach would require at least two minutes for registration and segmentation alone. The proposed method is automated and fast and requires no initialization as it works exclusively with FLAIR images. Such innovative methods will undoubtedly facilitate accurate and meaningful analysis of WMH in clinical practice by reducing complexity and increasing efficiency.

Optimizing outcomes in drug-resistant mesial temporal lobe epilepsy patients undergoing stereoelectroencephalography-guided radiofrequency thermocoagulation.

Background: Mesial temporal lobe epilepsy (MTLE) epileptiform discharges have been reported to arise from the hippocampus or the extrahippocampal medial temporal cortex, such as the amygdala, and then propagate to the temporal lobe cortex. The surgical ablation of which of these structures would result in a better postoperative outcome is debatable. Objective: To assess the possible factors that might have influenced the postoperative outcome of a group of drug-resistant mesial MTLE patients who underwent stereoelectroencephalography (SEEG)-guided radiofrequency thermocoagulation (RFTC). Design: Single-center, retrospective. Methods: The present study utilized a pre- and postoperative gray matter voxel-by-voxel ablation mapping comparison approach, along with a white matter mapping of longitudinal changes in the native space technique, to evaluate the association between the post-SEEG implantation signal recordings (obtained from clinically relevant electrode contacts used during RFTC) and the post-RFTC ablation volume of the different selected regions of interest (ROIs). Results: The study included 22 patients (12 men and 10 women, mean age 28.86 ± 14.04 years). Sixteen patients (72.72%) were seizure-free (SF), and six patients (27.27%) were non-SF. Five patients (22.72%) experienced mild side effects following RFTC. The post-RFTC follow-up period varied from 12 to 48 months, with an average of 24.17 ± 9.86 months. The SF group was associated with a higher number of implanted electrode contacts in the amygdala that were used during RFTC, a larger preoperative volume of the amygdala; a larger ablation volume of both the amygdala and rhinal cortex. The ablation volume of the white matter was statistically similar between both groups. Conclusion: This study provides valuable insights into the significance of the amygdala and rhinal cortex as ROIs in the preoperative evaluation of patients with MTLE. Future implantation scheme plans should consider evaluating the preoperative volume of these ROIs. Additionally, increasing the number of electrode contacts implanted within these regions might be beneficial to capture more clinically relevant signals and enhance their ablation volume.

Perinatal Caffeine Administration Improves Outcomes in an Ovine Model of Neonatal Hypoxia-Ischemia.

BACKGROUND: Neonatal hypoxic-ischemic encephalopathy disproportionately affects low- and middle-income countries, where ≈96% of affected infants reside. The current standard of care, therapeutic hypothermia, is frequently ineffective in this setting, likely because injury may be occurring earlier during labor. Here, we studied the pharmacokinetics, safety, and efficacy of perinatal caffeine administration in near-term lambs following global ischemic injury to support the development of earlier treatment strategies targeting the fetus in utero as well as the infant postnatally. METHODS: Ewes were randomly assigned to receive either 1 g IV caffeine citrate or placebo before delivery and placental transport assessed. Near-term lambs (141-143 days) of both sexes were subjected to severe global hypoxia-ischemia utilizing an acute umbilical cord occlusion model. Lambs that received caffeine in utero also received 20 mg/kg IV caffeine citrate following resuscitation and 10 mg/(kg·d) IV for 2 days. An additional cohort received 60 mg/kg followed by 30 mg/(kg·d) (low dose versus high dose) postnatally. Biochemical, histological, and neurological outcome measures in lambs were assessed over a 6-day period. RESULTS: Perinatal caffeine administration demonstrated excellent placental transport kinetics and was well tolerated with lamb plasma levels comparable to those targeted in neonates with apnea of prematurity. Caffeine administration resulted in a systemic immunomodulatory effect, evidenced by significant reductions in proinflammatory IP-10 levels. Treated lambs demonstrated improved neurodevelopmental outcomes, while histological analysis revealed that caffeine reduced gray matter injury and attenuated inflammation in the cingulate and parasagittal cortex. This neuroprotective effect was greater and via a different mode of action than we previously reported for azithromycin. A higher caffeine dosing regimen demonstrated significant toxicity. CONCLUSIONS: Perinatal caffeine administration is well tolerated, attenuates systemic and brain inflammation, and contributes to improvements in histological and neurological outcomes in an ovine model of neonatal hypoxic-ischemic encephalopathy.

Impact of social isolation on change in brain volume in community-dwelling older Japanese people: The NEIGE Study.

BACKGROUND: Several studies have shown that social isolation is a risk factor for cognitive decline and dementia; however, its neurological mechanisms are not fully understood. Using longitudinal data, this study examined the effects of social isolation on hippocampal and total gray matter volumes in community-dwelling older Japanese individuals. METHODS: Data were obtained from the Neuron to Environmental Impact Across Generations (NEIGE) Study conducted in Tokamachi City (Niigata Prefecture, Japan), including 279 community-dwelling persons aged 65-84 years who underwent brain magnetic resonance imaging in 2017 and 2021 (male: 47.6 %; mean age: 73.0 years). We investigated two dimensions of social isolation: poor social networks and solitary living. RESULTS: Multiple regression analysis with inverse probability weighting showed that individuals with a social contact frequency of <1 time/week had a greater decrease in hippocampal volume than those with a contact frequency of more than or equal to 4 times/week, whereas those who lived alone tended to have a smaller decrease in hippocampal volume than those who lived with others. We found no association between the frequency of social contact, living alone, and total gray matter volume. Furthermore, there was no interaction between sex and age for any of the outcomes. CONCLUSION: Our longitudinal analysis suggested that the relationship between social isolation and dementia onset may be mediated by hippocampal atrophy; however, the direction of the influence depends on the isolation type. These findings are expected to contribute to the elucidation of the social mechanisms underlying dementia onset.