Hoffmann's sign in cervical spondylotic myelopathy patients: pathological insights from neuroimaging.

OBJECTIVE: Hoffmann's sign testing is a commonly used physical examination in clinical practice for patients with cervical spondylotic myelopathy (CSM). However, the pathophysiological mechanisms underlying its occurrence and development have not been thoroughly investigated. Therefore, the present study aimed to explore whether a positive Hoffmann's sign (PHS) in CSM patients is associated with spinal cord and brain remodeling and to identify potential neuroimaging biomarkers with diagnostic value. METHODS: Seventy-six patients with CSM and 40 sex- and age-matched healthy controls (HCs) underwent multimodal MRI. Based on the results of the Hoffmann's sign examination, patients were divided into two groups: those with a PHS (n = 38) and those with a negative Hoffmann's sign (NHS; n = 38). Quantification of spinal cord and brain structural and functional parameters of the participants was performed using various methods, including functional connectivity analysis, voxel-based morphometry, and atlas-based analysis based on functional MRI and structural MRI data. Furthermore, this study conducted a correlation analysis between neuroimaging metrics and neurological function and utilized a support vector machine (SVM) algorithm for the classification of PHS and NHS. RESULTS: In comparison with the NHS and HC groups, PHS patients exhibited significant reductions in the cross-sectional area and fractional anisotropy (FA) of the lateral corticospinal tract (CST), reticulospinal tract (RST), and fasciculus cuneatus, concomitant with bilateral reductions in the volume of the lateral pallidum. The functional connectivity analysis indicated a reduction in functional connectivity between the left lateral pallidum and the right angular gyrus in the PHS group. The correlation analysis indicated a significant positive association between the CST and RST FA and the volume of the left lateral pallidum in PHS patients. Furthermore, all three variables exhibited a positive correlation with the patients' motor function. Finally, using multimodal neuroimaging metrics in conjunction with the SVM algorithm, PHS and NHS were classified with an accuracy rate of 85.53%. CONCLUSIONS: This research revealed a correlation between structural damage to the pallidum and RST and the presence of Hoffmann's sign as well as the motor function in patients with CSM. Features based on neuroimaging indicators have the potential to serve as biomarkers for assessing the extent of neuronal damage in CSM patients.

Structural and functional brain alterations in subthreshold depression: A multimodal coordinate-based meta-analysis.

Imaging studies of subthreshold depression (StD) have reported structural and functional abnormalities in a variety of spatially diverse brain regions. However, there is no consensus among different studies. In the present study, we applied a multimodal meta-analytic approach, the Activation Likelihood Estimation (ALE), to test the hypothesis that StD exhibits spatially convergent structural and functional brain abnormalities compared to healthy controls. A total of 31 articles with 25 experiments were included, collectively representing 1001 subjects with StD. We found consistent differences between StD and healthy controls mainly in the left insula across studies with various neuroimaging methods. Further exploratory analyses found structural atrophy and decreased functional activities in the right pallidum and thalamus in StD, and abnormal spontaneous activity converged to the middle frontal gyrus. Coordinate-based meta-analysis found spatially convergent structural and functional impairments in StD. These findings provide novel insights for understanding the neural underpinnings of subthreshold depression and enlighten the potential targets for its early screening and therapeutic interventions in the future.

Multimodal neuroimaging network associated with executive function in adolescent major depressive disorder patients via cognition-guided magnetic resonance imaging fusion.

Adolescents are high-risk population for major depressive disorder. Executive dysfunction emerges as a common feature of depression and exerts a significant influence on the social functionality of adolescents. This study aimed to identify the multimodal co-varying brain network related to executive function in adolescent with major depressive disorder. A total of 24 adolescent major depressive disorder patients and 43 healthy controls were included and completed the Intra-Extra Dimensional Set Shift Task. Multimodal neuroimaging data, including the amplitude of low-frequency fluctuations from resting-state functional magnetic resonance imaging and gray matter volume from structural magnetic resonance imaging, were combined with executive function using a supervised fusion method named multimodal canonical correlation analysis with reference plus joint independent component analysis. The major depressive disorder showed more total errors than the healthy controls in the Intra-Extra Dimensional Set Shift task. Their performance on the Intra-Extra Dimensional Set Shift Task was negatively related to the 14-item Hamilton Rating Scale for Anxiety score. We discovered an executive function-related multimodal fronto-occipito-temporal network with lower amplitude of low-frequency fluctuation and gray matter volume loadings in major depressive disorder. The gray matter component of the identified network was negatively related to errors made in Intra-Extra Dimensional Set Shift while positively related to stages completed. These findings may help to deepen our understanding of the pathophysiological mechanisms of cognitive dysfunction in adolescent depression.

Bridging Genetic Insights with Neuroimaging in Autism Spectrum Disorder-A Systematic Review.

Autism Spectrum Disorder (ASD) is an early onset neurodevelopmental disorder characterized by impaired social interaction and communication, and repetitive patterns of behavior. Family studies show that ASD is highly heritable, and hundreds of genes have previously been implicated in the disorder; however, the etiology is still not fully clear. Brain imaging and electroencephalography (EEG) are key techniques that study alterations in brain structure and function. Combined with genetic analysis, these techniques have the potential to help in the clarification of the neurobiological mechanisms contributing to ASD and help in defining novel therapeutic targets. To further understand what is known today regarding the impact of genetic variants in the brain alterations observed in individuals with ASD, a systematic review was carried out using Pubmed and EBSCO databases and following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. This review shows that specific genetic variants and altered patterns of gene expression in individuals with ASD may have an effect on brain circuits associated with face processing and social cognition, and contribute to excitation-inhibition imbalances and to anomalies in brain volumes.

Neuroimaging Features of Cytokine-related Diseases.

Cytokines are small secreted proteins that have specific effects on cellular interactions and are crucial for functioning of the immune system. Cytokines are involved in almost all diseases, but as microscopic chemical compounds they cannot be visualized at imaging for obvious reasons. Several imaging manifestations have been well recognized owing to the development of cytokine therapies such as those with bevacizumab (antibody against vascular endothelial growth factor) and chimeric antigen receptor (CAR) T cells and the establishment of new disease concepts such as interferonopathy and cytokine release syndrome. For example, immune effector cell-associated neurotoxicity is the second most common form of toxicity after CAR T-cell therapy toxicity, and imaging is recommended to evaluate the severity. The emergence of COVID-19, which causes a cytokine storm, has profoundly impacted neuroimaging. The central nervous system is one of the systems that is most susceptible to cytokine storms, which are induced by the positive feedback of inflammatory cytokines. Cytokine storms cause several neurologic complications, including acute infarction, acute leukoencephalopathy, and catastrophic hemorrhage, leading to devastating neurologic outcomes. Imaging can be used to detect these abnormalities and describe their severity, and it may help distinguish mimics such as metabolic encephalopathy and cerebrovascular disease. Familiarity with the neuroimaging abnormalities caused by cytokine storms is beneficial for diagnosing such diseases and subsequently planning and initiating early treatment strategies. The authors outline the neuroimaging features of cytokine-related diseases, focusing on cytokine storms, neuroinflammatory and neurodegenerative diseases, cytokine-related tumors, and cytokine-related therapies, and describe an approach to diagnosing cytokine-related disease processes and their differentials. ©RSNA, 2024 Supplemental material is available for this article.

Modern Magnetic Resonance Imaging Modalities to Advance Neuroimaging in Astronauts.

INTRODUCTION: The rapid development of the space industry requires a deeper understanding of spaceflight's impact on the brain. MRI research reports brain volume changes following spaceflight in astronauts, potentially affecting cognition. Recently, we have demonstrated that this evidence of volumetric changes, as measured by typical T1-weighted sequences (e.g., magnetization-prepared rapid gradient echo sequence; MPRAGE), is error-prone due to the microgravity-related redistribution of cerebrospinal fluid in the brain. More modern neuroimaging methods, particularly dual-echo MPRAGE (DEMPRAGE) and magnetization-prepared rapid gradient echo sequence utilizing two inversion pulses (MP2RAGE), have been suggested to be resilient to this error. Here, we tested if these imaging modalities offered consistent segmentation performance improvements in some commonly employed neuroimaging software packages.METHODS: We conducted manual gray matter tissue segmentation in traditional T1w MRI images to utilize for comparison. Automated tissue segmentation was performed for traditional T1w imaging, as well as on DEMPRAGE and MP2RAGE images from the same subjects. Statistical analysis involved a comparison of total gray matter volumes for each modality, and the extent of tissue segmentation agreement was assessed using a test of similarity (Dice coefficient).RESULTS: Neither DEMPRAGE nor MP2RAGE exhibited consistent segmentation performance across all toolboxes tested.DISCUSSION: This research indicates that customized data collection and processing methods are necessary for reliable and valid structural MRI segmentation in astronauts, as current methods provide erroneous classification and hence inaccurate claims of neuroplastic brain changes in the astronaut population.Berger L, Burles F, Jaswal T, Williams R, Iaria G. Modern magnetic resonance imaging modalities to advance neuroimaging in astronauts. Aerosp Med Hum Perform. 2024; 95(5):245-253.

Non-Specific Epileptic Activity, EEG, and Brain Imaging in Loss of Function Variants in SATB1: A New Case Report and Review of the Literature.

SATB1 (MIM #602075) is a relatively new gene reported only in recent years in association with neurodevelopmental disorders characterized by variable facial dysmorphisms, global developmental delay, poor or absent speech, altered electroencephalogram (EEG), and brain abnormalities on imaging. To date about thirty variants in forty-four patients/children have been described, with a heterogeneous spectrum of clinical manifestations. In the present study, we describe a new patient affected by mild intellectual disability, speech disorder, and non-specific abnormalities on EEG and neuroimaging. Family studies identified a new de novo frameshift variant c.1818delG (p.(Gln606Hisfs*101)) in SATB1. To better define genotype-phenotype associations in the different types of reported SATB1 variants, we reviewed clinical data from our patient and from the literature and compared manifestations (epileptic activity, EEG abnormalities and abnormal brain imaging) due to missense variants versus those attributable to loss-of-function/premature termination variants. Our analyses showed that the latter variants are associated with less severe, non-specific clinical features when compared with the more severe phenotypes due to missense variants. These findings provide new insights into SATB1-related disorders.

Building diagnostic neuroimaging biomarkers for psychiatric disorders using reverse inference approaches: A viable route?

The advent of structural magnetic resonance imaging (sMRI) at the end of the 20th century opened the way toward a deeper understanding of the neurophysiology of psychiatric disorders, substantiating regional structural abnormalities underlying this group of clinical conditions. However, despite abundant and flourishing scientific research, sMRI methodologies are not currently integrated into daily diagnostic practice. One reason behind this failed translation may be the prevailing approach to logical reasoning in neuroimaging: The forward inference via frequentist-based statistics. This reasoning prevents clinicians from obtaining information about the selectivity of results, which are therefore of limited use regarding the definition of biomarkers and refinement of diagnostic processes. Recently, another type of inferential approach has started to emerge in the neuroimaging field: The reverse inference via Bayesian statistics. Here, we introduce the key concepts of this approach, with a particular emphasis on the clinical sMRI environment. We survey recent findings showing significant potential for clinical translation. Clinical opportunities and challenges for developing reverse inference-based neural markers for psychiatry are also discussed. We propose that a systematic sharing of imaging data across the human brain mapping community is an essential first step toward a paradigmatic clinical shift. We conclude that a defined synergy between forward-based and reverse-based sMRI research can illuminate current discussions on diagnostic brain markers, offering clarity on key issues and fostering new tailored diagnostic avenues.

Understanding the impact of congenital infections and perinatal viral exposures on the developing brain using white matter magnetic resonance imaging: a scoping review.

BACKGROUND: Magnetic Resonance Imaging (MRI)-based imaging techniques are useful for assessing white matter (WM) structural and microstructural integrity in the context of infection and inflammation. The purpose of this scoping review was to assess the range of work on the use of WM neuroimaging approaches to understand the impact of congenital and perinatal viral infections or exposures on the developing brain. METHODS: This scoping review was conducted according to the Arksey and O' Malley framework. A literature search was performed in Web of Science, Scopus and PubMed for primary research articles published from database conception up to January 2022. Studies evaluating the use of MRI-based WM imaging techniques in congenital and perinatal viral infections or exposures were included. Results were grouped by age and infection. RESULTS: A total of 826 articles were identified for screening and 28 final articles were included. Congenital and perinatal infections represented in the included studies were cytomegalovirus (CMV) infection (n = 12), human immunodeficiency virus (HIV) infection (n = 11) or exposure (n = 2) or combined (n = 2), and herpes simplex virus (HSV) infection (n = 1). The represented MRI-based WM imaging methods included structural MRI and diffusion-weighted and diffusion tensor MRI (DWI/ DTI). Regions with the most frequently reported diffusion metric group differences included the cerebellar region, corticospinal tract and association fibre WM tracts in both children with HIV infection and children who are HIV-exposed uninfected. In qualitative imaging studies, WM hyperintensities were the most frequently reported brain abnormality in children with CMV infection and children with HSV infection. CONCLUSION: There was evidence that WM imaging techniques can play a role as diagnostic and evaluation tools assessing the impact of congenital infections and perinatal viral exposures on the developing brain. The high sensitivity for identifying WM hyperintensities suggests structural brain MRI is a useful neurodiagnostic modality in assessing children with congenital CMV infection, while the DTI changes associated with HIV suggest metrics such as fractional anisotropy have the potential to be specific markers of subtle impairment or WM damage in neuroHIV.

Parkinson's Disease Recognition Using Decorrelated Convolutional Neural Networks: Addressing Imbalance and Scanner Bias in rs-fMRI Data.

Parkinson's disease (PD) is a neurodegenerative and progressive disease that impacts the nerve cells in the brain and varies from person to person. The exact cause of PD is still unknown, and the diagnosis of PD does not include a specific objective test with certainty. Although deep learning has made great progress in medical neuroimaging analysis, these methods are very susceptible to biases present in neuroimaging datasets. An innovative decorrelated deep learning technique is introduced to mitigate class bias and scanner bias while simultaneously focusing on finding distinguishing characteristics in resting-state functional MRI (rs-fMRI) data, which assists in recognizing PD with good accuracy. The decorrelation function reduces the nonlinear correlation between features and bias in order to learn bias-invariant features. The publicly available Parkinson's Progression Markers Initiative (PPMI) dataset, referred to as a single-scanner imbalanced dataset in this study, was used to validate our method. The imbalanced dataset problem affects the performance of the deep learning framework by overfitting to the majority class. To resolve this problem, we propose a new decorrelated convolutional neural network (DcCNN) framework by applying decorrelation-based optimization to convolutional neural networks (CNNs). An analysis of evaluation metrics comparisons shows that integrating the decorrelation function boosts the performance of PD recognition by removing class bias. Specifically, our DcCNN models perform significantly better than existing traditional approaches to tackle the imbalance problem. Finally, the same framework can be extended to create scanner-invariant features without significantly impacting the performance of a model. The obtained dataset is a multiscanner dataset, which leads to scanner bias due to the differences in acquisition protocols and scanners. The multiscanner dataset is a combination of two publicly available datasets, namely, PPMI and FTLDNI-the frontotemporal lobar degeneration neuroimaging initiative (NIFD) dataset. The results of t-distributed stochastic neighbor embedding (t-SNE) and scanner classification accuracy of our proposed feature extraction-DcCNN (FE-DcCNN) model validated the effective removal of scanner bias. Our method achieves an average accuracy of 77.80% on a multiscanner dataset for differentiating PD from a healthy control, which is superior to the DcCNN model trained on a single-scanner imbalanced dataset.

A Narrative Review on the Clinical Relevance of Imaging the Circumventricular Brain Organs and Performing Their Anatomical and Histopathological Examination in Acute and Postacute COVID-19.

ABSTRACT: Autopsy followed by histopathological examination is foundational in clinical and forensic medicine for discovering and understanding pathological changes in disease, their underlying processes, and cause of death. Imaging technology has become increasingly important for advancing clinical research and practice, given its noninvasive, in vivo and ex vivo applicability. Medical and forensic autopsy can benefit greatly from advances in imaging technology that lead toward minimally invasive, whole-brain virtual autopsy. Brain autopsy followed by histopathological examination is still the hallmark for understanding disease and a fundamental modus operandi in forensic pathology and forensic medicine, despite the fact that its practice has become progressively less frequent in medical settings. This situation is especially relevant with respect to new diseases such as COVID-19 caused by the SARS-CoV-2 virus, for which our neuroanatomical knowledge is sparse. In this narrative review, we show that ad hoc clinical autopsies and histopathological analyses combined with neuroimaging of the principal circumventricular organs are critical to gaining insight into the reconstruction of the pathophysiological mechanisms and the explanation of cause of death (ie, atrium mortis) related to the cardiovascular effects of SARS-CoV-2 infection in forensic and clinical medicine.

[Neuroimaging-based study of scalp acupuncture targets for common psychiatric diseases].

Scalp acupuncture is a unique acupuncture method, developed based on the cerebral cortex localization. Neuroimaging technology enables the combination of contemporary brain science findings with the studies of scalp stimulation sites. In this study, based on the neuroimaging literature retrieved from Neurosynth platform, the scalp stimulation targets of common psychiatric diseases are developed, which provides the stimulation target protocol of scalp acupuncture for anxiety, bipolar disorder, major depressive disorder and post-traumatic stress disorder. The paper introduces the functions of the brain areas that are involved in each target and closely related to the diseases, and lists the therapeutic methods of common acupuncture and scalp acupuncture for each disease so as to provide the references for clinical practice. These targets can be used not only for the stimulation of scalp acupuncture, but also for the different neuromodulation techniques to treat related diseases.

Comparison of plasma and neuroimaging biomarkers to predict cognitive decline in non-demented memory clinic patients.

BACKGROUND: Plasma biomarkers of Alzheimer's disease (AD) pathology, neurodegeneration, and neuroinflammation are ideally suited for secondary prevention programs in self-sufficient persons at-risk of dementia. Plasma biomarkers have been shown to be highly correlated with traditional imaging biomarkers. However, their comparative predictive value versus traditional AD biomarkers is still unclear in cognitively unimpaired (CU) subjects and with mild cognitive impairment (MCI). METHODS: Plasma (Aß42/40, p-tau181, p-tau231, NfL, and GFAP) and neuroimaging (hippocampal volume, centiloid of amyloid-PET, and tau-SUVR of tau-PET) biomarkers were assessed at baseline in 218 non-demented subjects (CU = 140; MCI = 78) from the Geneva Memory Center. Global cognition (MMSE) was evaluated at baseline and at follow-ups up to 5.7 years. We used linear mixed-effects models and Cox proportional-hazards regression to assess the association between biomarkers and cognitive decline. Lastly, sample size calculations using the linear mixed-effects models were performed on subjects positive for amyloid-PET combined with tau-PET and plasma biomarker positivity. RESULTS: Cognitive decline was significantly predicted in MCI by baseline plasma NfL (ß=-0.55), GFAP (ß=-0.36), hippocampal volume (ß = 0.44), centiloid (ß=-0.38), and tau-SUVR (ß=-0.66) (all p < 0.05). Subgroup analysis with amyloid-positive MCI participants also showed that only NfL and GFAP were the only significant predictors of cognitive decline among plasma biomarkers. Overall, NfL and tau-SUVR showed the highest prognostic values (hazard ratios of 7.3 and 5.9). Lastly, we demonstrated that adding NfL to the inclusion criteria could reduce the sample sizes of future AD clinical trials by up to one-fourth in subjects with amyloid-PET positivity or by half in subjects with amyloid-PET and tau-PET positivity. CONCLUSIONS: Plasma NfL and GFAP predict cognitive decline in a similar manner to traditional imaging techniques in amyloid-positive MCI patients. Hence, even though they are non-specific biomarkers of AD, both can be implemented in memory clinic workups as important prognostic biomarkers. Likewise, future clinical trials might employ plasma biomarkers as additional inclusion criteria to stratify patients at higher risk of cognitive decline to reduce sample sizes and enhance effectiveness.

SAN: Mitigating spatial covariance heterogeneity in cortical thickness data collected from multiple scanners or sites.

In neuroimaging studies, combining data collected from multiple study sites or scanners is becoming common to increase the reproducibility of scientific discoveries. At the same time, unwanted variations arise by using different scanners (inter-scanner biases), which need to be corrected before downstream analyses to facilitate replicable research and prevent spurious findings. While statistical harmonization methods such as ComBat have become popular in mitigating inter-scanner biases in neuroimaging, recent methodological advances have shown that harmonizing heterogeneous covariances results in higher data quality. In vertex-level cortical thickness data, heterogeneity in spatial autocorrelation is a critical factor that affects covariance heterogeneity. Our work proposes a new statistical harmonization method called spatial autocorrelation normalization (SAN) that preserves homogeneous covariance vertex-level cortical thickness data across different scanners. We use an explicit Gaussian process to characterize scanner-invariant and scanner-specific variations to reconstruct spatially homogeneous data across scanners. SAN is computationally feasible, and it easily allows the integration of existing harmonization methods. We demonstrate the utility of the proposed method using cortical thickness data from the Social Processes Initiative in the Neurobiology of the Schizophrenia(s) (SPINS) study. SAN is publicly available as an R package.

Advancing post-traumatic seizure classification and biomarker identification: Information decomposition based multimodal fusion and explainable machine learning with missing neuroimaging data.

A late post-traumatic seizure (LPTS), a consequence of traumatic brain injury (TBI), can potentially evolve into a lifelong condition known as post-traumatic epilepsy (PTE). Presently, the mechanism that triggers epileptogenesis in TBI patients remains elusive, inspiring the epilepsy community to devise ways to predict which TBI patients will develop PTE and to identify potential biomarkers. In response to this need, our study collected comprehensive, longitudinal multimodal data from 48 TBI patients across multiple participating institutions. A supervised binary classification task was created, contrasting data from LPTS patients with those without LPTS. To accommodate missing modalities in some subjects, we took a two-pronged approach. Firstly, we extended a graphical model-based Bayesian estimator to directly classify subjects with incomplete modality. Secondly, we explored conventional imputation techniques. The imputed multimodal information was then combined, following several fusion and dimensionality reduction techniques found in the literature, and subsequently fitted to a kernel- or a tree-based classifier. For this fusion, we proposed two new algorithms: recursive elimination of correlated components (RECC) that filters information based on the correlation between the already selected features, and information decomposition and selective fusion (IDSF), which effectively recombines information from decomposed multimodal features. Our cross-validation findings showed that the proposed IDSF algorithm delivers superior performance based on the area under the curve (AUC) score. Ultimately, after rigorous statistical comparisons and interpretable machine learning examination using Shapley values of the most frequently selected features, we recommend the two following magnetic resonance imaging (MRI) abnormalities as potential biomarkers: the left anterior limb of internal capsule in diffusion MRI (dMRI), and the right middle temporal gyrus in functional MRI (fMRI).

Brain tumor detection with integrating traditional and computational intelligence approaches across diverse imaging modalities - Challenges and future directions.

Brain tumor segmentation and classification play a crucial role in the diagnosis and treatment planning of brain tumors. Accurate and efficient methods for identifying tumor regions and classifying different tumor types are essential for guiding medical interventions. This study comprehensively reviews brain tumor segmentation and classification techniques, exploring various approaches based on image processing, machine learning, and deep learning. Furthermore, our study aims to review existing methodologies, discuss their advantages and limitations, and highlight recent advancements in this field. The impact of existing segmentation and classification techniques for automated brain tumor detection is also critically examined using various open-source datasets of Magnetic Resonance Images (MRI) of different modalities. Moreover, our proposed study highlights the challenges related to segmentation and classification techniques and datasets having various MRI modalities to enable researchers to develop innovative and robust solutions for automated brain tumor detection. The results of this study contribute to the development of automated and robust solutions for analyzing brain tumors, ultimately aiding medical professionals in making informed decisions and providing better patient care.

Immunolabeling-compatible PEGASOS tissue clearing for high-resolution whole mouse brain imaging.

Novel brain clearing methods revolutionize imaging by increasing visualization throughout the brain at high resolution. However, combining the standard tool of immunostaining targets of interest with clearing methods has lagged behind. We integrate whole-mount immunostaining with PEGASOS tissue clearing, referred to as iPEGASOS (immunostaining-compatible PEGASOS), to address the challenge of signal quenching during clearing processes. iPEGASOS effectively enhances molecular-genetically targeted fluorescent signals that are otherwise compromised during conventional clearing procedures. Additionally, we demonstrate the utility of iPEGASOS for visualizing neurochemical markers or viral labels to augment visualization that transgenic mouse lines cannot provide. Our study encompasses three distinct applications, each showcasing the versatility and efficacy of this approach. We employ whole-mount immunostaining to enhance molecular signals in transgenic reporter mouse lines to visualize the whole-brain spatial distribution of specific cellular populations. We also significantly improve the visualization of neural circuit connections by enhancing signals from viral tracers injected into the brain. Last, we show immunostaining without genetic markers to selectively label beta-amyloid deposits in a mouse model of Alzheimer's disease, facilitating the comprehensive whole-brain study of pathological features.

[Features of the clinical and neuroimaging picture in patients with early-onset Alzheimer's disease]. / Osobennosti klinicheskoi i neirovizualizatsionnoi kartiny u patsientov s bolezn'yu Al'tsgeimera s rannim nachalom.

The most common cause of severe cognitive impairment in adults is Alzheimer's disease (AD). Depending on the age of onset, AD is divided into early (<65 years) and late (≥65 years) forms. Early-onset AD (EOAD) is significantly less common than later-onset AD (LOAD) and accounts for only about 5-10% of cases. However, its medical and social significance, as a disease leading to loss of ability to work and legal capacity, as well as premature death in patients aged 40-64 years, is extremely high. Patients with EOAD compared with LOAD have a greater number of atypical clinical variants - 25% and 6-12.5%, respectively, which complicates the differential diagnosis of EOAD with other neurodegenerative diseases. However, the typical classical amnestic variant predominates in both EOAD and LOAD. Also, patients with EOAD have peculiarities according to neuroimaging data: when performing MRI of the brain, patients with EOAD often have more pronounced parietal atrophy and less pronounced hippocampal atrophy compared to patients with LOAD. The article pays attention to the features of the clinical and neuroimaging data in patients with EOAD; a case of a patient with EOAD is presented.

Closing the loop between environment, brain and mental health: how far we might go in real-life assessments?

PURPOSE OF REVIEW: Environmental factors such as climate, urbanicity, and exposure to nature are becoming increasingly important influencers of mental health. Incorporating data gathered from real-life contexts holds promise to substantially enhance laboratory experiments by providing a more comprehensive understanding of everyday behaviors in natural environments. We provide an up-to-date review of current technological and methodological developments in mental health assessments, neuroimaging and environmental sensing. RECENT FINDINGS: Mental health research progressed in recent years towards integrating tools, such as smartphone based mental health assessments or mobile neuroimaging, allowing just-in-time daily assessments. Moreover, they are increasingly enriched by dynamic measurements of the environment, which are already being integrated with mental health assessments. To ensure ecological validity and accuracy it is crucial to capture environmental data with a high spatio-temporal granularity. Simultaneously, as a supplement to experimentally controlled conditions, there is a need for a better understanding of cognition in daily life, particularly regarding our brain's responses in natural settings. SUMMARY: The presented overview on the developments and feasibility of "real-life" approaches for mental health and brain research and their potential to identify relationships along the mental health-environment-brain axis informs strategies for real-life individual and dynamic assessments.