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Objective: To conduct an inductive analysis of neuroimaging studies following the global popularity surrounding the effects of acupuncture., Methods: The relevant works of literature on acupuncture effects-related neuroimaging studies were retrieved from the web of science core collection database from the date of its establishment to 2022. Bibexcel software was used to extract information, such as article numbers, countries, journals, agencies, funding sources and keywords. The VOSviewer software's co-occurrence and clustering module was used for the co-occurrence and cluster analysis of collaborative networks and high frequency keywords, such as authors, institutions and countries., Results: Neuroimaging research on the effects of acupuncture began in the 1960 s and has since received the extensive attention of scholars. China, The United States and South Korea conduct more neuroimaging research on the effects of acupuncture than other countries. Academic journals such as Evidence-Based Complementary and Alternative Medicine, Journal of Alternative and Complementary Medicine, Neural Regeneration Research and Acupuncture in Medicine have published the highest number of acupuncture-related neuroimaging articles. Moreover, cluster analysis differentiates high-frequency keywords into six clusters. Furthermore, the use of the keyword 'density atlas' reveals that neuroimaging research on the effects of acupuncture surrounding pain and neurofunctional rehabilitation is currently the research focus., Conclusions: This bibliometric study identifies six important research hot spots: Static brain functional connectivity analysis of acupuncture analgesic effects, key theories on the scientific problems of acupuncture, acupuncture analgesic effect of the placebo effect, the neuroimaging of acupoint ST-36, the value of acupuncture in regulating the autonomic nerves and acupuncture therapy as complementary medicine., Competing Interests: Conflict of interest No conflict of interest exists in this study., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Objective: This study aims to compare the management practices of a headache specialist with non-headache specialists in the treatment of children with migraine. The use of appropriate rescue medications and prophylactic agents, application of neuroimaging, and short-term outcomes are compared in children treated by the two groups of physicians., Methods: A retrospective cohort study was conducted by utilizing the electronic medical records of children 3-18 years of age with migraine, who were evaluated at a tertiary care children's hospital from 2016 to 2018., Results: Of the 849 patients who met the study criteria, 469 children were classified as having chronic migraine or high-frequency episodic migraine and were followed-up on at least 1 occasion by the neurologists. Imaging was obtained in 66.5% of all children with migraine. The headache specialist used 5-HT agonists ("triptans") for migraine management in 56.7% (76/135) of cases compared to non-headache specialists who prescribed them in 28.7% (96/334) of cases (P < .001). Of the children with chronic migraine, the headache specialist evaluated 135 patients while the non-headache specialists treated 334 children. Non-headache specialists prescribed prophylaxis in the form of natural supplements more frequently (63.8% of cases) compared to the headache specialist (38.5% of children) (P < .001). Moreover, prophylaxis with prescription drugs was utilized more often by headache specialist (66.7%) than non-headache specialists (37.4%) (P < .001)., Conclusions: Imaging appears to be commonly recommended by both headache specialists and non-headache specialists in children with migraine. The headache specialist was more likely to use triptans as rescue medications for pediatric migraine. Outcomes in the short-term were not statistically different whether children were being managed by the headache specialist or the non-headache specialists., (© 2019 American Headache Society.)

Neurocognitive disorders leading to progressive cognitive, functional and behavioural impairment are often undiagnosed or diagnosed lately. But tailored care and therapeutics help in implementing secondary and tertiary prevention dynamics aiming at preserving quality of life and delaying, anticipating or preventing behavioural crisis and severe stages of dementia. Moreover, the diagnosis of numerous diseases induces specific care and therapeutics, as well access to research and clinical trials. For the first time, the representatives of the National College of General Practitioners, the French Federation of Memory Centres, the French Federation of Gerontology and Geriatrics, the French Federation of Neurology, the French Society of Psychogeriatrics and the national plan on neurodegenerative diseases propose a graduated and tailored diagnosis strategy involving primary care and specialists of neurocognitive disorders. This strategy has been built in the context of the national plan on neurodegenerative diseases, the European Joint Action "Act on dementia", and has been consensually agreed after a seminar animated by the National College of General Practitioners in March 2017., (Copyright © 2017 Elsevier Masson SAS. All rights reserved.)

Abstract Despite advancements, the prevalence of HIV-associated neurocognitive impairment remains at approximately 40%, attributed to factors like pre-cART (combination antiretroviral therapy) irreversible brain injury. People with HIV (PWH) treated with cART do not show significant neurocognitive changes over relatively short follow-up periods. However, quantitative neuroimaging may be able to detect ongoing subtle microstructural changes. In this study, we hypothesized that tensor-valued diffusion encoding metrics would provide greater sensitivity than conventional diffusion tensor imaging (DTI) metrics in detecting HIV-associated brain microstructural injury. We further hypothesized that tensor-valued metrics would exhibit stronger associations with blood markers of neuronal and glial injury, such as neurofilament light chain (NFL) and glial fibrillary acidic protein (GFAP), as well as with cognitive performance. Using MRI at 3T, 24 PWH and 31 healthy controls underwent cross-sectional examination. The results revealed significant variations in tensor-valued diffusion encoding metrics across white matter regions, with associations observed between these metrics, cognitive performance, NFL and GFAP. Moreover, a significant interaction between HIV status and imaging metrics in gray and white matter was observed, particularly impacting total cognitive scores. Of interest, DTI metrics were less likely to be associated with HIV status than tensor-valued diffusion metrics. These findings suggest that tensor-valued diffusion encoding metrics offer heightened sensitivity in detecting subtle changes associated with axonal injury in HIV infection. Longitudinal studies are needed to further evaluate responsiveness of tensor-valued diffusion b-tensor encoding metrics in the contest HIV-associate mild chronic neuroinflammation.

Abstract The isocitrate dehydrogenase (IDH) mutation status is one of the most important markers according to the 2021 WHO classification of CNS tumors. Preoperatively, this information is usually obtained based on invasive biopsies, contrast-enhanced MR images or PET images generated using radioactive tracers. However, the completely non-invasive determination of IDH mutation status using routinely acquired preoperative native CT images has hardly been investigated to date. In our study, we show that radiomics-based machine learning allows to determine IDH mutation status based on preoperative native CT images both with very high accuracy and completely non-invasively. Based on independent test data, we are able to correctly identify 91.1% of cases with an IDH mutation. Our final model, containing only six features, exhibits a high area under the curve of 0.847 and an excellent area under the precision-recall curve of 0.945. In the future, such models may be used for a completely non-invasive prediction of important genetic markers, potentially allowing treating physicians to reduce the number of biopsies and speed up further treatment planning.

Trigeminal neuralgia presents significant challenges in neurosurgery, with patients responding variably to Microvascular Decompression (MVD). Advanced neuroimaging techniques, specifically MRI using the Constructive Interference in Steady State (CISS) sequence and Diffusion Tensor Imaging (DTI), have emerged as promising tools in predicting the outcomes of MVD. Aim of the study was to determine neuroimaging predictors of successful microvascular decompression surgery in patients with classical trigeminal neuralgia. Material and methods. This retrospective study analyzed 58 patients who underwent microvascular decompression for classical trigeminal neuralgia at the Federal Neurosurgical Center in Novosibirsk, Russia, between January 2017 and December 2021. Outcomes were assessed by the Barrow Neurological Institution (BNI) scale. Preoperative neuroimaging with CISS and DTI was used to evaluate neurovascular conflict severity, nerve diffusion and other criteria. Results. 79.3 % (BNI I & II) of patients were practically free of pain postoperatively, in which 65.5 % (BNI I) had successfully completely recovered from the procedure with no pain, and 13.8 % (BNI II) had a good outcome. And the study found a significant correlation between favorable MVD outcomes and higher Sindou grades 2 and 3, indicating more severe neurovascular conflict (p < 0.001). Also significant differences were observed in outcomes based on fractional anisotropy values on symptomatic nerve (p = 0.029). Discussion. Anatomical factors which measured by MRI had limited impact on outcomes, while Sindou grading showed a significant correlation with surgical outcomes, and higher fractional anisotropy values were associated with poorer outcomes. Conclusions. The investigation of neuroimaging predictors for MVD surgery outcomes in patients with trigeminal neuralgia, received from CISS-sequence imaging and pre-treatment DTI, indicates the importance of estimation of severity of vascular compression in surgical planning and the predictive value of fractional anisotropy values from DTI for preoperative assessments.

Abstract Background Although numerous studies have shown that bariatric surgery results in sustained weight loss and modifications in gut microbiota composition and cognitive function, the exact underlying mechanisms are unclear. This study aimed to investigate the effects of bariatric surgery on cognitive function through the microbiota-gut-brain axis (MGBA). Methods Demographic data, serum samples, fecal samples, cognitive assessment scales, and resting-state functional connectivity magnetic resonance imaging (rs-fMRI) scans were obtained from 39 obese patients before and after (6 months) laparoscopic sleeve gastrectomy (LSG). PCA analysis, OPLS-DA analysis, and permutation tests were used to conduct fecal 16 S microbiota profiling, serum metabolomics, and neuroimaging analyses, and a bariatric surgery-specific rs-fMRI brain functional connectivity network was constructed. Spearman correlation analysis and Co-inertia analysis were employed to correlate significant alterations in cognitive assessment scales and resting-state functional connectivity difference networks with differential serum metabolites and 16 S microbiota data to identify key gut microbiota and serum metabolic factors. Results LSG significantly reduced the weight of obese patients, with reductions of up to 28%. Furthermore, cognitive assessment scale measurements revealed that LSG enhanced cognitive functions, including memory (HVLT, p = 0.000) and executive function (SCWT, p = 0.008). Also, LSG significantly altered gut microbiota composition (p = 0.001), with increased microbial abundance and diversity (p 1). Spearman correlation analysis among cognitive assessment scales, gut microbiota species, and serum metabolites revealed correlations with 68 gut microbiota species and 138 serum metabolites (p

Hidden information is the key to many security issues. If there is a reliable method to determine whether someone withholds information, many issues of this type can be resolved. However, until now, no method has proven to be reliable, but technical discoveries in the field of neuroimaging have caused a surge of new research in this area. Many neuroimaging techniques can be used, but functional magnetic resonance is the newest method, and its use in extracting and evaluating information from subjects could be the most significant, given that it records brain states in parallel with current mental activity/behavior, enabling the establishment of correlational links between them. Because the brain state displayed during fMRI imaging is the dependent variable measured during stimulus/task condition manipulation, it is necessary to use fMRI data in combination with complementary criminal interrogation techniques to gather information. This could be particularly important when standard interrogational techniques are not enough in order to preserve the common good, especially in “ticking bomb” situations. In this study, we review aspects of the possibility of utilizing advanced neuroimaging in combination with criminal interrogation in cases of serious criminal acts that threaten public safety.

Abstract Hyperhomocysteinemia or high levels (> 15 µmol/L) of homocysteine (Hcy)in the blood has been suggested to affect the brain through vascular and neurodegenerative pathways and potentially impact cognition. The current study aims to explore the association of high homocysteine with cognition and brain volume changes in a cohort of middle and old agedr adults. The study recruited 1296 participants aged ≥ 45 years from Tata Longitudinal Study of Ageing (TLSA), an ongoing cohort study. The participants underwent detailed cognitive assessments using Addenbrooke’s Cognitive Examination-III (ACE-III) and Computerized Assessment of Adult Information Processing (COGNITO) neuropsychological battery and MR imaging using a 3T scanner. The participants were classified based on the median homocysteine level (16.89 µmol/L) into low Hcy (≤ median) and high Hcy (> median) groups. When adjusted for age, gender, years of education, vitamin B12, folate and dyslipidaemia, Generalised Linear Model (GLM) found a significant association of high Hcy with vocabulary task [β (95% CI) − 1.354 (− 2.655, − 0.052); p = 0.041]. Significant associations was also obtained between cerebral white matter volume and high Hcy [β (95% CI) − 5617.182 (− 11062.762, − 173.602); p = 0.043]. The results suggest that people with high Hcy levels performed poorer in cognitive tasks related to language domain and had lesser cerebral white matter volume. This indicates that homocysteine might have a profound impact on brain structure as well as function.

Abstract Cognitive impairments, which can be caused by neurodegenerative and cerebrovascular disease, represent a growing global health crisis with far‐reaching implications for individuals, families, healthcare systems, and economies worldwide. Notably, neurodegenerative‐induced cognitive impairment often presents a different pattern and severity compared to cerebrovascular‐induced cognitive impairment. With the development of computational technology, machine learning techniques have developed rapidly, which offers a powerful tool in radiomic analysis, allowing a more comprehensive model that can handle high‐dimensional, multivariate data compared to the traditional approach. Such models allow the prediction of the disease development, as well as accurately classify disease from overlapping symptoms, therefore facilitating clinical decision making. This review will focus on the application of machine learning‐based radiomics on cognitive impairment caused by neurogenerative and cerebrovascular disease. Within the neurodegenerative category, this review primarily focuses on Alzheimer's disease, while also covering other conditions such as Parkinson's disease, Lewy body dementia, and Huntington's disease. In the cerebrovascular category, we concentrate on poststroke cognitive impairment, including ischemic and hemorrhagic stroke, with additional attention given to small vessel disease and moyamoya disease. We also review the specific challenges and limitations when applying machine learning radiomics, and provide our suggestion to overcome those limitations towards the end, and discuss what could be done for future clinical use.

Pain is a private experience observable through various verbal and non-verbal behavioural manifestations, each of which may relate to different pain-related functions. Despite the importance of understanding the cerebral mechanisms underlying those manifestations, there is currently limited knowledge of the neural correlates of the facial expression of pain. In this functional magnetic resonance imaging (fMRI) study, noxious heat stimulation was applied in healthy volunteers and we tested if previously published brain signatures of pain were sensitive to pain expression. We then applied a multivariate pattern analysis to the fMRI data to predict the facial expression of pain. Results revealed the inability of previously developed pain neurosignatures to predict the facial expression of pain. We thus propose a facial expression of pain signature (FEPS) conveying distinctive information about the brain response to nociceptive stimulations with minimal or no overlap with other pain-relevant brain signatures associated with nociception, pain ratings, thermal pain aversiveness, or pain valuation. The FEPS may provide a distinctive functional characterization of the distributed cerebral response to nociceptive pain associated with the socio-communicative role of non-verbal pain expression. This underscores the complexity of pain phenomenology by reinforcing the view that neurosignatures conceived as biomarkers must be interpreted in relation to the specific pain manifestation(s) predicted and their underlying function(s). Future studies should explore other pain-relevant manifestations and assess the specificity of the FEPS against simulated pain expressions and other types of aversive or emotional states.

Bobble-Head Doll Syndrome (BHDS) is a rare neurological disorder characterized by repetitive, involuntary, and stereotypical head movements, resembling the motion of a bobble-head doll, often accompanied by various neurological and endocrinological symptoms. Although primarily observed in pediatric patients, BobbleHead Doll Syndrome can also manifest in adults. The pathophysiology remains unclear, but it is often associated with suprasellar cysts, third ventricle cysts, or aqueductal stenosis. Diagnosis typically involves magnetic resonance imaging to visualize the cystic lesions and assess cerebrospinal fluid flow. Additional diagnostic modalities include computed tomography and electromyography. Treatment primarily involves neurosurgical intervention, such as ventriculocystostomy or ventriculocystocisternostomy, aimed at relieving cerebrospinal fluid obstruction. Despite the challenges in diagnosis and management, early recognition and appropriate treatment offer promising outcomes, including symptom resolution and potential recovery. This review provides a comprehensive overview of Bobble-Head Doll Syndrome, including its clinical presentation, pathogenesis, diagnostic approach, and therapeutic strategies, highlighting the need for further research to elucidate its underlying mechanisms and optimize therapeutic strategies.

Abstract Background Sex differences exist in the prevalence and clinical manifestation of several mental disorders, suggesting that sex-specific brain phenotypes may play key roles. Previous research used machine learning models to classify sex from imaging data of the whole brain and studied the association of class probabilities with mental health, potentially overlooking regional specific characteristics. Methods We here investigated if a regionally constrained model of brain volumetric imaging data may provide estimates that are more sensitive to mental health than whole brain-based estimates. Given its known role in emotional processing and mood disorders, we focused on the limbic system. Using two different cohorts of healthy subjects, the Human Connectome Project and the Queensland Twin IMaging, we investigated sex differences and heritability of brain volumes of limbic structures compared to non-limbic structures, and subsequently applied regionally constrained machine learning models trained solely on limbic or non-limbic features. To investigate the biological underpinnings of such models, we assessed the heritability of the obtained sex class probability estimates, and we investigated the association with major depression diagnosis in an independent clinical sample. All analyses were performed both with and without controlling for estimated total intracranial volume (eTIV). Results Limbic structures show greater sex differences and are more heritable compared to non-limbic structures in both analyses, with and without eTIV control. Consequently, machine learning models performed well at classifying sex based solely on limbic structures and achieved performance as high as those on non-limbic or whole brain data, despite the much smaller number of features in the limbic system. The resulting class probabilities were heritable, suggesting potentially meaningful underlying biological information. Applied to an independent population with major depressive disorder, we found that depression is associated with male–female class probabilities, with largest effects obtained using the limbic model. This association was significant for models not controlling for eTIV whereas in those controlling for eTIV the associations did not pass significance correction. Conclusions Overall, our results highlight the potential utility of regionally constrained models of brain sex to better understand the link between sex differences in the brain and mental disorders.

Abstract Prior research has shown that the sensorimotor cortical oscillations are uncharacteristic in persons with cerebral palsy (CP); however, it is unknown if these altered cortical oscillations have an impact on adaptive sensorimotor control. This investigation evaluated the cortical dynamics when the motor action needs to be changed “on-the-fly”. Adults with CP and neurotypical controls completed a sensorimotor task that required either proactive or reactive control while undergoing magnetoencephalography (MEG). When compared with the controls, the adults with CP had a weaker beta (18–24 Hz) event-related desynchronization (ERD), post-movement beta rebound (PMBR, 16–20 Hz) and theta (4–6 Hz) event-related synchronization (ERS) in the sensorimotor cortices. In agreement with normative work, the controls exhibited differences in the strength of the sensorimotor gamma (66–84 Hz) ERS during proactive compared to reactive trials, but similar condition-wise changes were not seen in adults with CP. Lastly, the adults with CP who had a stronger theta ERS tended to have better hand dexterity, as indicated by the Box and Blocks Test and Purdue Pegboard Test. These results may suggest that alterations in the theta and gamma cortical oscillations play a role in the altered hand dexterity and uncharacteristic adaptive sensorimotor control noted in adults with CP.

Abstract Parkinson's disease (PD) is a progressive neurodegenerative disorder affecting over 3% of those over 65. It's caused by reduced dopaminergic neurons and Lewy bodies, leading to motor and non-motor symptoms. The relationship between COMT gene polymorphisms and PD is complex and not fully elucidated. Some studies have reported associations between certain COMT gene variants and PD risk, while others have not found significant associations. This study investigates how COMT gene variations impact cortical thickness changes in PD patients over time, aiming to link genetic factors, especially COMT gene variations, with PD progression. This study analyzed data from 44 PD patients with complete 4-year imaging follow-up from the Parkinson Progression Marker Initiative (PPMI) database. Magnetic resonance imaging (MRI) scans were acquired using consistent methods across 9 different MRI scanners. COMT single-nucleotide polymorphisms (SNPs) were assessed based on whole genome sequencing data. Longitudinal image analysis was conducted using FreeSurfer's processing pipeline. Linear mixed-effect models were employed to examine the interaction effect of genetic variations and time on cortical thickness, while controlling for covariates and subject-specific variations. The rs165599 SNP stands out as a potential contributor to alterations in cortical thickness, showing a significant reduction in overall mean cortical thickness in both hemispheres in homozygotes (Left: P = 0.023, Right: P = 0.028). The supramarginal, precentral, and superior frontal regions demonstrated significant bilateral alterations linked to rs165599. Our findings suggest that the rs165599 variant leads to earlier manifestation of cortical thinning during the course of the disease. However, it does not result in more severe cortical thinning outcomes over time. There is a need for larger cohorts and control groups to validate these findings and consider genetic variant interactions and clinical features to elucidate the specific mechanisms underlying COMT-related neurodegenerative processes in PD.

Abstract Sensory impairment and brain atrophy is common among older adults, increasing the risk of dementia. Yet, the degree to which multiple co-occurring sensory impairments (MSI across vision, proprioception, vestibular function, olfactory, and hearing) are associated with brain morphometry remain unexplored. Data were from 208 cognitively unimpaired participants (mean age 72 ± 10 years; 59% women) enrolled in the Baltimore Longitudinal Study of Aging. Multiple linear regression models were used to estimate cross-sectional associations between MSI and regional brain imaging volumes. For each additional sensory impairment, there were associated lower orbitofrontal gyrus and entorhinal cortex volumes but higher caudate and putamen volumes. Participants with MSI had lower mean volumes in the superior frontal gyrus, orbitofrontal gyrus, superior parietal lobe, and precuneus compared to participants with

Abstract Chronic musculoskeletal pain including knee osteoarthritis (OA) is a leading cause of disability worldwide. Previous research indicates ethnic-race groups differ in the pain and functional limitations experienced with knee OA. However, when socioenvironmental factors are included in analyses, group differences in pain and function wane. Pain-related brain structures are another area where ethnic-race group differences have been observed. Environmental and sociocultural factors e.g., income, education, experiences of discrimination, and social support influence brain structures. We investigate if environmental and sociocultural factors reduce previously observed ethnic-race group differences in pain-related brain structures. Data were analyzed from 147 self-identified non-Hispanic black (NHB) and non-Hispanic white (NHW), middle and older aged adults with knee pain in the past month. Information collected included health and pain history, environmental and sociocultural resources, and brain imaging. The NHB adults were younger and reported lower income and education compared to their NHW peers. In hierarchical multiple regression models, sociocultural and environmental factors explained 6–37% of the variance in pain-related brain regions. Self-identified ethnicity-race provided an additional 4–13% of explanatory value in the amygdala, hippocampus, insula, bilateral primary somatosensory cortex, and thalamus. In the rostral/caudal anterior cingulate and dorsolateral prefrontal cortex, self-identified ethnicity-race was not a predictor after accounting for environmental, sociocultural, and demographic factors. Findings help to disentangle and identify some of the factors contributing to ethnic-race group disparities in pain-related brain structures. Numerous arrays of environmental and sociocultural factors remain to be investigated. Further, the differing sociodemographic representation of our NHB and NHW participants highlights the role for intersectional considerations in future research.

In the era of machinery, Artificial Intelligence (AI) has become the new tool for managing patients in psychiatry. Nowadays, many psychiatric disorders are being diagnosed and treated with the help of AI. New technologies like Machine Learning (ML), robots, Deep Learning (DL), and sensor-based systems provide a different perspective on psychiatric disorders. The present narrative review article summarised the use of AI in diagnosing and treating psychiatric disorders. AI can assist a patients with a psychiatric diseases in prognosis, clinical diagnosis, management therapy, and addressing clinical and technological issues. It highlights various AI methods used in mental healthcare, with a focus on multiple ML perspectives. Additionally, AI has the potential to address several factors, including privacy, transparency, bias, and other social and ethical considerations. The aim of the present review was to redefine mental disorders more objectively, personalise treatments, facilitate early diagnosis, and provide patients with more choices in their care. Through the present article, author aimed to highlight the use of AI in the diagnosis of various psychiatric disorders such as depression, schizophrenia, bipolar disorder, Autism Spectrum Disorder (ASD), and Alzheimer’s Disease (AD).

Substance use, including cigarettes and cannabis, is associated with poorer sustained attention in late adolescence and early adulthood. Previous studies were predominantly cross-sectional or under-powered and could not indicate if impairment in sustained attention was a predictor of substance use or a marker of the inclination to engage in such behavior. This study explored the relationship between sustained attention and substance use across a longitudinal span from ages 14 to 23 in over 1000 participants. Behaviors and brain connectivity associated with diminished sustained attention at age 14 predicted subsequent increases in cannabis and cigarette smoking, establishing sustained attention as a robust biomarker for vulnerability to substance use. Individual differences in network strength relevant to sustained attention were preserved across developmental stages and sustained attention networks generalized to participants in an external dataset. In summary, brain networks of sustained attention are robust, consistent, and able to predict aspects of later substance use.

Abstract Background and Aims Stroke is a prominent cause of long‐term adult impairment globally and a significant global health issue. Only 14% of stroke survivors achieve full recovery, while 25% to 50% require varying degrees of support, and over half become dependent. The aftermath of a stroke brings profound changes to an individual's life, with early choices significantly impacting their quality of life. This review aims to establish the efficacy of neuroimaging data in predicting long‐term outcomes and recovery rates following a stroke. Methods A scientific literature search was conducted using the Centre of Reviews and Dissemination (CRD) criteria and PRISMA guidelines for a combined meta‐narrative and systematic quantitative review. The methodology involved a structured search in databases like PubMed and The Cochrane Library, following inclusion and exclusion criteria to identify relevant studies on neuroimaging biomarkers for stroke outcome prediction. Data collection utilized the Microsoft Edge Zotero plugin, with quality appraisal conducted via the CASP checklist. Studies published from 2010 to 2024, including observational, randomized control trials, case reports, and clinical trials. Non‐English and incomplete studies were excluded, resulting in the identification of 11 pertinent articles. Data extraction emphasized study methodologies, stroke conditions, clinical parameters, and biomarkers, aiming to provide a thorough literature overview and evaluate the significance of neuroimaging biomarkers in predicting stroke recovery outcomes. Results The results of this systematic review indicate that integrating advanced neuroimaging methods with highly successful reperfusion therapies following a stroke facilitates the diagnosis of the condition and assists in improving neurological impairments resulting from stroke. These measures reduce the possibility of death and improve the treatment provided to stroke patients. Conclusion These findings highlight the crucial role of neuroimaging in advancing our understanding of post‐stroke outcomes and improving patient care.

Long-COVID afflicts millions with relentless fatigue, disrupting daily life. The objective of this narrative review is to synthesize current evidence on the role of the basal ganglia in long-COVID fatigue, discuss potential mechanisms, and highlight promising therapeutic interventions. A comprehensive literature search was conducted using PubMed, Scopus, and Web of Science databases. Mounting evidence from PET, MRI, and functional connectivity data reveals basal ganglia disturbances in long-COVID exhaustion, including inflammation, metabolic disruption, volume changes, and network alterations focused on striatal dopamine circuitry regulating motivation. Theories suggest inflammation-induced signaling disturbances could impede effort/reward valuation, disrupt cortical–subcortical motivational pathways, or diminish excitatory input to arousal centers, attenuating drive initiation. Recent therapeutic pilots targeting basal ganglia abnormalities show provisional efficacy. However, heterogeneous outcomes, inconsistent metrics, and perceived versus objective fatigue discrepancies temper insights. Despite the growing research, gaps remain in understanding the precise pathways linking basal ganglia dysfunction to fatigue and validating treatment efficacy. Further research is needed to advance understanding of the basal ganglia’s contribution to long-COVID neurological sequelae and offer hope for improving function across the expanding affected population.

Objective: The aim of this study was to investigate the demographic and clinical findings of patients monitored at the newly established Epilepsy Outpatient Clinic at the University of Health Sciences Turkey, Adana Medical Faculty, Adana City Training and Research Hospital. Methods: A total of 315 patients who were monitored between July 2021 and October 2022 at the Neurology Clinic’s Epilepsy Outpatient Clinic of the University of Health Sciences Turkey, Adana Medical Faculty, Adana City Training and Research Hospital were included in the study. Results: Of the patients, 161 were male (51.1%) and 154 were female (48.9%). The average age of the patients was 33.9±13.98, and the average seizure frequency was 33.51±98.49 per year. The most frequently observed risk factors included a family history of epilepsy (31.3%), febrile convulsions (19.7%), and head trauma (18.4%). Neuroimaging findings were pathological in 47.6% of the patients, and electroencephalography findings were pathological in 65.8%. Focal onset seizures were observed in 45.1% of the patients, while generalized onset seizures were seen in 46.7%. Monotherapy was administered to 52.7% of the patients, and polytherapy was given to 41.6%. In epilepsy patients aged eighteen and above, pathological findings in neuroimaging were significantly higher (p

Background: Hypertensive disorders remain among the most significant complications of pregnancy. Neurological complications of eclampsia are a major contributor to morbidity and mortality that is associated with eclampsia. Neuroimaging studies have revolutionized visualization of hypertensive encephalopathy aids in timely intervention and a favorable maternal and perinatal outcome. Aims and Objectives: (1) To study the spectrum of neuroimaging findings in patients with eclampsia. (2) To correlate these findings with the maternal outcome. Materials and Methods: A prospective observational study was undertaken in the Department of Obstetrics and Gynecology, Mysore Medical College and research institute Mysore, for 18 months. All the eclamptic mothers during the study were included in the study. They were studied in terms of neuroimaging and its correlation with maternal outcomes. Results: The incidence of eclampsia was 7.14% which is relatively high, attributed to the fact that being a tertiary care center many cases are referred. Higher incidence was seen in primigravida (72.4%) and lower maternal age of 18–20 years (48%). Cesarean section was indicated in 50% of deliveries indicating immediate action was necessary for better fetomaternal outcome. They presented with varied imminent symptoms such as headache in 20.4%, vomiting in 22.4%, and visual blurring in 22.4%. About 50% presented with altered sensorium. On non-contrast computed tomography brain, 64.3% (63) had normal findings, 17.3% (17) had posterior reversible encephalopathy syndrome (PRES) with a sensitivity of 68.42% and specificity of 85%, whereas on magnetic resonance imaging (MRI) brain, 36.26% had no abnormalities, 40.81% had PRES with a sensitivity of 86.84%, and specificity of 85%. About 58.6% had an uneventful maternal outcome. The others were hemolysis, elevated liver enzymes, and low platelet syndrome seen in 9.18% of patients, acute kidney injury in 6.12%, postpartum hemorrhage seen in 5.1%. The maternal mortality rate was 7.66%, the most common cause of death was intracranial hemorrhage. Conclusion: The common neuroimaging findings in eclampsia are cerebral edema, PRES, cerebral venous thrombosis, infarcts, hemorrhage, and hypertensive leukoencephalopathy. Although some abnormalities seen in neuroimaging studies are incidental and transient without chronic neurologic sequelae, both CT and MRI findings correlate with the clinical presentation and maternal outcome but MRI correlates better compared to CT and can be a better imaging modality in eclampsia patients and is indicated in all patients of eclampsia.

For patients with cognitive impairment as the main clinical manifestation, structural MRI (or CT instead) should be performed first to clarify intracranial lesions and brain atrophy. For patients with specific clinical manifestations, specific MRI sequences is recommended to further assist diagnosis. If the patient is suspected of AD, it is recommended to perform oblique coronal T1WI for MTA grading to score medial temporal lobe atrophy. If the patient is suspected to be caused by vascular factors or special infections (prion proteins), it is recommended to perform diffusion-weighted imaging. If the patient has extrapyramidal symptoms or small vessel disease, especially cerebral amyloid angiopathy or cognitive impairment complicated by diabetes, it is recommended to perform susceptibility-weighted imaging. If a mass is suspicious on MRI, contrast-enhanced MR imaging and MR spectroscopy should be performed. If the patient has amyotrophic lateral sclerosis, it is recommended to perform diffusion tensor imaging. If the patient is suspected to be caused by neurodegenerative diseases, it is recommended to perform 18F-FDG PET and Aβ-PET or tau-PET. Aβ-PET imaging and tau-PET imaging can visualize the degree and scope of pathological protein deposition in the brain, which has important predictive and diagnostic value for dementia and can be used for the differential diagnosis of dementia and staging the disease progression. In addition, resting-state functional magnetic resonance imaging, near-infrared spectroscopy, and some emerging imaging techniques such as cine phase-contrast magnetic resonance imaging, and diffusion tensor image analysis along the perivascular space have been studied in patients with cognitive impairment. It is expected that these technologies can be used in the future to better assist the diagnosis and differential diagnosis of cognitive impairment. It should be noted that neuroimaging does not represent the complete diagnosis and clinical symptoms of the disease and must be interpreted with caution.

Understanding emotions and sensations from a neuroscientific context is essential for unravelling the complex mechanisms underlying human behaviour and cognition. Emotions are created by maintaining a positive or negative relationship of an individual towards the experiences and facts of reality. Sensations are the result of the influence of the material world on our senses or receptors. Recent advancements in neuroimaging techniques, such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) have provided unprecedented insights into the neural circuitry underlying emotions and sensations. Additionally the role of neurotransmitters, neural networks and brain regions are implicated in emotion regulation, sensory processing and their integration. Furthermore, there is impact of emotional and sensory dysregulation on various psychiatric disorders, including anxiety, depression and post-traumatic stress disorder (PTSD), highlighting potential avenues for therapeutic interventions. Overall, the importance of interdisciplinary research is in bridging the gap between neuroscience and psychology, paving the way for a deeper understanding of human emotions and sensations which is reflected in both simple experiences is emphasised.

Despite major advancements in our understanding of its fundamental causes, pain—both acute and chronic—remains a serious health concern. Various preclinical investigations utilizing diverse animal, cellular, and alternative models are required and frequently demanded by regulatory approval bodies to bridge the gap between the lab and the clinic. Investigating naturally occurring painful disorders can speed up medication development at the preclinical and clinical levels by illuminating molecular pathways. A wide range of animal models related to pain have been developed to elucidate pathophysiological mechanisms and aid in identifying novel targets for treatment. Pain sometimes drugs fail clinically, causing high translational costs due to poor selection and the use of preclinical tools and reporting. To improve the study of pain in a clinical context, researchers have been creating innovative models over the past few decades that better represent pathological pain conditions. In this paper, we provide a summary of traditional animal models, including rodents, cellular models, human volunteers, and alternative models, as well as the specific characteristics of pain diseases they model. However, a more rigorous approach to preclinical research and cutting-edge analgesic technologies may be necessary to successfully create novel analgesics. The research highlights from this review emphasize new opportunities to develop research that includes animals and non-animals using proven methods pertinent to comprehending and treating human suffering. This review highlights the value of using a variety of modern pain models in animals before human trials. These models can help us understand the different mechanisms behind various pain types. This will ultimately lead to the development of more effective pain medications.

Aim: Acute neurological complaints are one of the reasons why children present to pediatric emergency departments. Neuro-imaging techniques gain more importance in children since physical examination and anamnesis do not provide sufficient information due to insufficient cooperation. We aimed to determine the distribution of patients who applied to the pediatric emergency department with non-traumatic acute neurological complaints and underwent neu-roimaging, and the clinical benefit of neuroimaging in these patients. Methods: The information and records of the patients who applied to the Konya City Hospital Pediatric Emergency Clinic between January 1 and October 1, 2022, due to acute neurological complaints and underwent neuroimaging were retrieved and analyzed retrospectively. Acute neurological complaints of the patients were classified according to the International Classifi-cation of Diseases-10 diagnostic coding. Results: This study included 180 (50.5% male) patients. The median age of the patients was 120 (interquartile range: 45-180) months. Afebrile convulsion was the most common reason for admission in 69 patients (38.3%). Cranial computed tomography (CCT) was performed in all 180 patients. Of the patients, 68 (37.8%) only underwent CCT scan, while 90 (50%) had diffusion magnetic resonance imaging (MRI), 20 (11.1%) had brain+diffusion MRI, and 2 (1.1%) had brain+diffusion+spinal MRI. Neuroimaging abnormalities were statistically higher in patients with abnormal physical examination findings than in patients with normal physical examination findings (p

In this review, we will discuss the neuroimaging findings of patients with COVID-19 from the outbreak (late December 2019) to the end of October 2021. PubMed, Scopus, Google Scholar, Science Direct, ProQuest, Web of Science and the World Health Organization database (January 01, 2020, to October 30, 2021) were searched for related published articles. In each of the databases, the appendix search strategies were performed and the below keywords were used: COVID-19”OR“coronavirus disease 2019” AND “brain MRI” OR “brain magnetic resonance imaging” OR “brain CT” OR “neuroimaging”. In total, neuroimaging findings of 1550 patients, with ages from 1-96 years, were analyzed. Most brain neuroimaging findings include hyperintensity, Cerebral venous thrombosis, intraventricular and subarachnoid hemorrhage, infarction, leukoencephalopathy, acute ischemic strokes and posterior reversible encephalopathy syndrome (PRES) in adult patients and severe encephalopathy, stroke, infarction, CNS infection/demyelination, neuritis or polyradiculitis, venous thrombosis, Guillain-Barré syndrome, and longitudinally extensive myelitis, and myositis in pediatric patients. Our findings showed that the most important complication of the coronavirus is not just respiratory complications, because although transiently, COVID-19-related brain complications are seen in pediatrics as well as adults, and families should pay more attention to health care.

Objectives: The aim of the present study was to compare the features of the structural complexity of the cerebral hemispheres in men and women using fractal analysis of outlined and skeletonized images, as well as quantitative analysis of digital skeletons of the cerebral hemispheres. Material and Methods: Magnetic resonance imaging brain scans of 100 individuals aged 18-86 years (44 males and 56 females) were investigated. Five tomographic sections of each brain were selected for morphometric study (4 coronal and 1 axial sections). The sections were preprocessed, and outlined and skeletonized images were obtained. Fractal analysis was conducted using the two-dimensional box counting method, and fractal dimensions of outlined and skeletonized images were determined. Additionally, quantitative analysis of skeletonized images was performed, determining the following parameters: branches, junctions, end-point voxels, junction voxels, slab voxels, triple points, quadruple points, average branch length, and maximum branch length. Results: We observed that both variants of fractal dimension in males and females did not show significant differences, although most quantitative parameters in males were larger than those in females. Conclusions: The spatial and structural complexity of the cerebral hemispheres, as characterized by fractal dimensions, is almost indistinguishable between males and females. However, in some individual tomographic sections, the male brain may exhibit a slightly higher number of end-point voxels, corresponding to the gyri of the cerebral hemispheres. The obtained data can be used in clinical practice for diagnostic purposes (e.g., for detecting malformations) and for theoretical studies in neuroanatomy.

Abstract Introduction Neuroimaging has revealed that migraine is linked to alterations in both the structure and function of the brain. However, the relationship of these changes with aging has not been studied in detail. Here we employ the Brain Age framework to analyze migraine, by building a machine-learning model that predicts age from neuroimaging data. We hypothesize that migraine patients will exhibit an increased Brain Age Gap (the difference between the predicted age and the chronological age) compared to healthy participants. Methods We trained a machine learning model to predict Brain Age from 2,771 T1-weighted magnetic resonance imaging scans of healthy subjects. The processing pipeline included the automatic segmentation of the images, the extraction of 1,479 imaging features (both morphological and intensity-based), harmonization, feature selection and training inside a 10-fold cross-validation scheme. Separate models based only on morphological and intensity features were also trained, and all the Brain Age models were later applied to a discovery cohort composed of 247 subjects, divided into healthy controls (HC, n=82), episodic migraine (EM, n=91), and chronic migraine patients (CM, n=74). Results CM patients showed an increased Brain Age Gap compared to HC (4.16 vs -0.56 years, P=0.01). A smaller Brain Age Gap was found for EM patients, not reaching statistical significance (1.21 vs -0.56 years, P=0.19). No associations were found between the Brain Age Gap and headache or migraine frequency, or duration of the disease. Brain imaging features that have previously been associated with migraine were among the main drivers of the differences in the predicted age. Also, the separate analysis using only morphological or intensity-based features revealed different patterns in the Brain Age biomarker in patients with migraine. Conclusion The brain-predicted age has shown to be a sensitive biomarker of CM patients and can help reveal distinct aging patterns in migraine.

Advances in medical imaging in the last several years have paved the way for new applications outside healthcare, such as in fields such as lie detection and people screening. However, these applications outside of therapeutic settings raise ethical concerns regarding privacy, permission, and potential abuse. The current limitations of medical imaging in various contexts are examined in this paper, which highlights the need for robust research protocols, regulatory frameworks, and informed consent processes.1 Neuro-ethical concerns are tackled to ensure the development and use of new neuro-technologies in a way that respects people's autonomy and is ethical. Lie Detection and Ethical Concerns False positive detection using functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) during interrogations pertaining to national security and criminal investigations has attracted a lot of interest.2 In their view, these methods could improve security and help solve crimes by tracking brain activity patterns linked with telling the truth or lying. 3 However, owing to factors such as small sample sizes, controlled laboratory settings, and individual heterogeneity in brain activity patterns, the scientific validity of imaging-based lie detection is still in dispute. Further, major ethical concerns arise when people are subjected to intrusive brain scans without their knowledge or agreement. Involuntary subjecting of detainees or suspects to such testing raises concerns about potential violations of informed consent and civil liberties, as well as the disproportionate impact on marginalized communities and the widening of existing justice system gaps. Because the technology is still in its early stages, there is a great chance of misunderstandings and false allegations. Pre-employment Screening and Ethical Concerns The use of medical imaging for the purpose of screening potential employees is another non-clinical application. Scans, according to proponents, could reveal personality attributes—like the ability to manage one's impulses or emotions—that are important for a certain job. Most studies depend on small samples and group averages instead of individual forecasts, hence the scientific evidence supporting this assertion is weak.4 In addition, the human aspect is lost when people are reduced to biological statistics, which ignores the intricacies of human behavior. Members of already-vulnerable populations may feel further pressure to submit to sexually explicit brain scans for hiring reasons, even when they have not given their informed consent. Due to the present constraints, the predictive efficacy and ethical acceptability of screening with medical imaging are seriously doubted. Informed Consent and Privacy Concerns In non-clinical uses of medical imaging, getting really informed permission is a major concern.5 Subjects under coercion may feel forced to submit to scans during interrogations or when their participation is crucial to a hiring decision. It is possible that even in study settings, participants do not completely understand the consequences of their agreement, especially when it comes to the prospect of their brain data being accessed and examined in the future due to technological advancements.6 Strong privacy protections are required because sensitive medical information, if obtained, could be misused or disclosed without authorization. Neuroimaging data must be protected against possible misuse and abuse by implementing stringent governance and oversight structures. 7 Although there are interesting possibilities for medical imaging outside of healthcare, rushing into adoption could severely compromise people's freedoms and independence. What is scientifically valid today can be proven wrong tomorrow because the technology is continually evolving.8 Current methodological constraints in neuroscience must be addressed through stringent research standards incorporating large representative samples before non-clinical or commercial utilization.9 Robust governance mechanisms should be put in place to prevent misuse, and consent processes should be tightened to make sure people are making really informed decisions. Responsible development and application of developing neuro-technologies requires open, interdisciplinary dialogue between neuro-ethicists, legal specialists, legislators, and society at large. We must seriously address the ethical obligations that accompany the ever-increasing capabilities of medical imaging. Particularly for uses outside of direct treatment, safeguarding individual privacy and consent ought to take precedence. Respecting human autonomy and societal well-being is essential for responsibly developing and applying medical imaging technologies. Only then can we fully utilize their promise.

Abstract Background The premonitory phase, or prodrome, of migraine, provides valuable opportunities to study attack initiation and for treating the attack before headache starts. Much that has been learned about this phase in recent times has come from the outcomes of functional imaging studies. This review will summarise these studies to date and use their results to provide some feasible insights into migraine neurobiology. Main body The ability to scan repeatedly a patient without radiation and with non-invasive imaging modalities, as well as the recognition that human experimental migraine provocation compounds, such as nitroglycerin (NTG) and pituitary adenylate cyclase activating polypeptide (PACAP), can trigger typical premonitory symptoms (PS) and migraine-like headache in patients with migraine, have allowed feasible and reproducible imaging of the premonitory phase using NTG. Some studies have used serial scanning of patients with migraine to image the migraine cycle, including the ‘pre-ictal’ phase, defined by timing to headache onset rather than symptom phenotype. Direct observation and functional neuroimaging of triggered PS have also revealed compatible neural substrates for PS in the absence of headache. Various imaging methods including resting state functional MRI (rsfMRI), arterial spin labelling (ASL), positron emission tomography (PET) and diffusion tensor imaging (DTI) have been used. The results of imaging the spontaneous and triggered premonitory phase have been largely consistent and support a theory of central migraine attack initiation involving brain areas such as the hypothalamus, midbrain and limbic system. Early dysfunctional pain, sensory, limbic and homeostatic processing via monoaminergic and peptidergic neurotransmission likely manifests in the heterogeneous PS phenotype. Conclusion Advances in human migraine research, including the use of functional imaging techniques lacking radiation or radio-isotope exposure, have led to an exciting opportunity to study the premonitory phase using repeated measures imaging designs. These studies have provided novel insights into attack initiation, migraine neurochemistry and therapeutic targets. Emerging migraine-specific therapies, such as those targeting calcitonin gene-related peptide (CGRP), are showing promise acutely when taken during premonitory phase to reduce symptoms and prevent subsequent headache. Therapeutic research in this area using PS for headache onset prediction and early treatment is likely to grow in the future.

Obesity is a major public health problem that requires new approaches. Despite all interventions, the behavioural and therapeutic interventions developed have demonstrated limited effectiveness in curbing the obesity epidemic. Findings from imaging studies of the brain suggest the existence of neural vulnerabilities and structural changes that are associated with the development of obesity and eating disorders. This review highlights the clinical relevance of brain neuroimaging research in obese individuals to prevent risky behaviour, early diagnosis, and the development of new safer and more effective treatments.

Abstract Background Schizophrenia is characterized by complex psychiatric symptoms and unclear pathological mechanisms. Most previous studies have focused on the morphological changes that occur over the development of the disease; however, the corresponding functional trajectories remain unclear. In the present study, we aimed to explore the progressive trajectories of patterns of dysfunction after diagnosis. Methods Eighty-six patients with schizophrenia and 120 healthy controls were recruited as the discovery dataset. Based on multiple functional indicators of resting-state brain functional magnetic resonance imaging, we conducted a duration-sliding dynamic analysis framework to investigate trajectories in association with disease progression. Neuroimaging findings were associated with clinical symptoms and gene expression data from the Allen Human Brain Atlas database. A replication cohort of patients with schizophrenia from the University of California, Los Angeles, was used as the replication dataset for the validation analysis. Results Five stage-specific phenotypes were identified. A symptom trajectory was characterized by positive-dominated, negative ascendant, negative-dominated, positive ascendant, and negative surpassed stages. Dysfunctional trajectories from primary and subcortical regions to higher-order cortices were recognized; these are associated with abnormal external sensory gating and a disrupted internal excitation–inhibition equilibrium. From stage 1 to stage 5, the importance of neuroimaging features associated with behaviors gradually shifted from primary to higher-order cortices and subcortical regions. Genetic enrichment analysis identified that neurodevelopmental and neurodegenerative factors may be relevant as schizophrenia progresses and highlighted multiple synaptic systems. Conclusions Our convergent results indicate that progressive symptoms and functional neuroimaging phenotypes are associated with genetic factors in schizophrenia. Furthermore, the identification of functional trajectories complements previous findings of structural abnormalities and provides potential targets for drug and non-drug interventions in different stages of schizophrenia.

Abstract Background The development of machine learning models for aiding in the diagnosis of mental disorder is recognized as a significant breakthrough in the field of psychiatry. However, clinical practice of such models remains a challenge, with poor generalizability being a major limitation. Methods Here, we conducted a pre-registered meta-research assessment on neuroimaging-based models in the psychiatric literature, quantitatively examining global and regional sampling issues over recent decades, from a view that has been relatively underexplored. A total of 476 studies (n = 118,137) were included in the current assessment. Based on these findings, we built a comprehensive 5-star rating system to quantitatively evaluate the quality of existing machine learning models for psychiatric diagnoses. Results A global sampling inequality in these models was revealed quantitatively (sampling Gini coefficient (G) = 0.81, p 15). In light of this, we proposed a purpose-built quantitative assessment checklist, which demonstrated that the overall ratings of these models increased by publication year but were negatively associated with model performance. Conclusions Together, improving sampling economic equality and hence the quality of machine learning models may be a crucial facet to plausibly translating neuroimaging-based diagnostic classifiers into clinical practice.

In vivo neuroimaging studies have established several reproducible volumetric sex differences in the human brain, but the causes of such differences are hard to parse. While mouse models are useful for understanding the cellular and mechanistic bases of sex-specific brain development, there have been no attempts to formally compare human and mouse neuroanatomical sex differences to ascertain how well they translate. Addressing this question would shed critical light on the use of the mouse as a translational model for sex differences in the human brain and provide insights into the degree to which sex differences in brain volume are conserved across mammals. Here, we use structural magnetic resonance imaging to conduct the first comparative neuroimaging study of sex-specific neuroanatomy of the human and mouse brain. In line with previous findings, we observe that in humans, males have significantly larger and more variable total brain volume; these sex differences are not mirrored in mice. After controlling for total brain volume, we observe modest cross-species congruence in the volumetric effect size of sex across 60 homologous regions (r=0.30). This cross-species congruence is greater in the cortex (r=0.33) than non-cortex (r=0.16). By incorporating regional measures of gene expression in both species, we reveal that cortical regions with greater cross-species congruence in volumetric sex differences also show greater cross-species congruence in the expression profile of 2835 homologous genes. This phenomenon differentiates primary sensory regions with high congruence of sex effects and gene expression from limbic cortices where congruence in both these features was weaker between species. These findings help identify aspects of sex-biased brain anatomy present in mice that are retained, lost, or inverted in humans. More broadly, our work provides an empirical basis for targeting mechanistic studies of sex-specific brain development in mice to brain regions that best echo sex-specific brain development in humans.

The cerebral cortex underlies many of our unique strengths and vulnerabilities, but efforts to understand human cortical organization are challenged by reliance on incompatible measurement methods at different spatial scales. Macroscale features such as cortical folding and functional activation are accessed through spatially dense neuroimaging maps, whereas microscale cellular and molecular features are typically measured with sparse postmortem sampling. Here, we integrate these distinct windows on brain organization by building upon existing postmortem data to impute, validate, and analyze a library of spatially dense neuroimaging-like maps of human cortical gene expression. These maps allow spatially unbiased discovery of cortical zones with extreme transcriptional profiles or unusually rapid transcriptional change which index distinct microstructure and predict neuroimaging measures of cortical folding and functional activation. Modules of spatially coexpressed genes define a family of canonical expression maps that integrate diverse spatial scales and temporal epochs of human brain organization – ranging from protein–protein interactions to large-scale systems for cognitive processing. These module maps also parse neuropsychiatric risk genes into subsets which tag distinct cyto-laminar features and differentially predict the location of altered cortical anatomy and gene expression in patients. Taken together, the methods, resources, and findings described here advance our understanding of human cortical organization and offer flexible bridges to connect scientific fields operating at different spatial scales of human brain research.

Acute stress can change our cognition and emotions, but what specific consequences this has for human prosocial behaviour is unclear. Previous studies have mainly investigated prosociality with financial transfers in economic games and produced conflicting results. Yet a core feature of many types of prosocial behaviour is that they are effortful. We therefore examined how acute stress changes our willingness to exert effort that benefits others. Healthy male participants – half of whom were put under acute stress – made decisions whether to exert physical effort to gain money for themselves or another person. With this design, we could independently assess the effects of acute stress on prosocial, compared to self-benefitting, effortful behaviour. Compared to controls (n = 45), participants in the stress group (n = 46) chose to exert effort more often for self- than for other-benefitting rewards at a low level of effort. Additionally, the adverse effects of stress on prosocial effort were particularly pronounced in more selfish participants. Neuroimaging combined with computational modelling revealed a putative neural mechanism underlying these effects: more stressed participants showed increased activation to subjective value in the dorsal anterior cingulate cortex and anterior insula when they themselves could benefit from their exerted effort relative to when someone else could. By using an effort-based task that better approximates real-life prosocial behaviour and incorporating trait differences in prosocial tendencies, our study provides important insights into how acute stress affects prosociality and its associated neural mechanisms.

Abstract Neurocysticercosis is an infection of the central nervous system caused by the larval stage of Taenia solium. Although endemic in sub‐Saharan Africa, it is neglected but remains a significant cause of preventable seizure in adults. Its diagnosis is challenging and is frequently missed due to its variable clinical manifestations and lack of diagnostic facilities in most areas of sub‐Saharan Africa. This report discusses two cases of parenchymal neurocysticercosis in Ghanaians who presented to the emergency unit of a District Hospital with adult‐onset seizures. The two cases highlight the need for a high index of suspicion and also underscore the important role of neuroimaging in the evaluation of patients presenting with adult‐onset seizures in neurocysticercosis endemic areas. This is necessary for prompt detection and initiation of appropriate therapy in order to improve prognosis.

Abstract Background Neuroimaging studies have made an important contribution to our understanding of headache pathophysiology. This systematic review aims to provide a comprehensive overview and critical appraisal of mechanisms of actions of headache treatments and potential biomarkers of treatment response disclosed by imaging studies. Main body We performed a systematic literature search on PubMed and Embase databases for imaging studies investigating central and vascular effects of pharmacological and non-pharmacological treatments used to abort and prevent headache attacks. Sixty-three studies were included in the final qualitative analysis. Of these, 54 investigated migraine patients, 4 cluster headache patients and 5 patients with medication overuse headache. Most studies used functional magnetic resonance imaging (MRI) (n = 33) or molecular imaging (n = 14). Eleven studies employed structural MRI and a few used arterial spin labeling (n = 3), magnetic resonance spectroscopy (n = 3) or magnetic resonance angiography (n = 2). Different imaging modalities were combined in eight studies. Despite of the variety of imaging approaches and results, some findings were consistent. This systematic review suggests that triptans may cross the blood–brain barrier to some extent, though perhaps not sufficiently to alter the intracranial cerebral blood flow. Acupuncture in migraine, neuromodulation in migraine and cluster headache patients, and medication withdrawal in patients with medication overuse headache could promote headache improvement by reverting headache-affected pain processing brain areas. Yet, there is currently no clear evidence for where each treatment acts, and no firm imaging predictors of efficacy. This is mainly due to a scarcity of studies and heterogeneous treatment schemes, study designs, subjects, and imaging techniques. In addition, most studies used small sample sizes and inadequate statistical approaches, which precludes generalizable conclusions. Conclusion Several aspects of headache treatments remain to be elucidated using imaging approaches, such as how pharmacological preventive therapies work, whether treatment-related brain changes may influence therapy effectiveness, and imaging biomarkers of clinical response. In the future, well-designed studies with homogeneous study populations, adequate sample sizes and statistical approaches are needed.

Aim: The aim of the present study was to characterize age-associated changes in the spatial configuration of cerebral hemispheres (including changes in spatial complexity and space-filling capacity) using fractal analysis of silhouette magnetic resonance brain images. Material and Methods: Magnetic resonance brain images of 100 (44 male, 56 female) participants aged between 18-86 years were studied. Five magnetic resonance images were selected from the magnetic resonance imaging dataset of each brain, including four tomographic sections in the coronal plane and one in the axial plane. Fractal dimension values of the cerebral hemispheres silhouettes were measured using the two-dimensional box-counting algorithm. Morphometric parameters based on Euclidean geometry (perimeter, area, and their derivative values) were determined as well. Results: The average fractal dimension value of the five studied tomographic sections was 1.878±0.0009, the average value of four coronal sections was 1.868±0.0010. It was shown that fractal dimension values of cerebral silhouettes for all studied tomographic sections and four coronal sections significantly decrease with age (r=-0.512, p

Acute bilateral blindness is an emergent condition that may signal life-threatening disease. The approach focuses on identification of life-threatening disease processes, while differentiating between ocular, psychogenic and neurologic aetiologies. We present the case of an 88-year-old man with multiple cardiovascular risk factors and bilateral chronic glaucoma and cataracts. He presented to the emergency department with sudden onset bilateral loss of visual acuity with no other relevant changes on physical examination, including other neurologic deficits. Ophthalmologic observation showed no sign of acute disease. Contrast orbit and cranioencephalic CT was unremarkable, so the patient underwent an orbit and cranioencephalic MRI that showed changes in fluid-attenuated inversion recovery (FLAIR) sequences and diffusion restriction involving the optic chiasm and the initial segment of the optic radiations bilaterally. Optic chiasm strokes are rare, owing to the rich supply of collateral circulation. The most frequent presentation is bitemporal hemianopsia but rarer presentations are described. Bilateral loss of visual acuity is very rare and infarction of the whole optic chiasm is unusual.

AbstractBackground Cerebrovascular disease is a significant cause of morbidity and mortality in Africa, and using neuroimaging techniques has improved the diagnosis and management of this disease. However, there is a lack of comprehensive reviews of the role and effectiveness of neuroimaging techniques in the African context.Methods We reviewed the literature to evaluate the role of neuroimaging in diagnosing and managing cerebrovascular disease in Africa. Our search included electronic databases such as PubMed, Scopus, and Google Scholar from 2000 to April 2023. We included peer-reviewed studies written in English that reported on the use of neuroimaging in diagnosing and managing cerebrovascular disease in African populations. We excluded non-peer-reviewed articles, letters, editorials, and studies unrelated to cerebrovascular disease, neuroimaging, or Africa. A total of 102 potential articles were identified; after applying our exclusion criteria and removing duplicated articles, 51 articles were reviewed.Results Our findings suggest that neuroimaging techniques such as CT, MRI, and Skull x-ray play a crucial role in diagnosing and managing cerebrovascular disease in Africa. CT and MRI were the most commonly used techniques, with CT being more widely available and less expensive than MRI. However, challenges to using neuroimaging in Africa include the high cost of equipment and maintenance, lack of trained personnel, and inadequate infrastructure. These challenges limit the widespread use of neuroimaging in diagnosing and managing cerebrovascular disease in Africa.Conclusion Neuroimaging techniques are essential for diagnosing and managing cerebrovascular disease in Africa, but challenges to their use must be addressed to improve healthcare outcomes. Our policy recommendations can help improve the availability and accessibility of neuroimaging services in Africa.

IntroductionIn preclinical studies, menopausal elevations in pituitary gonadotropins, follicle-stimulating hormone (FSH) and luteinizing hormone (LH), trigger Alzheimer's disease (AD) pathology and synaptic loss in female animals. Herein, we took a translational approach to test whether gonadotropin elevations are linked to AD pathophysiology in women.MethodsWe examined 191 women ages 40–65 years, carrying risk factors for late-onset AD, including 45 premenopausal, 67 perimenopausal, and 79 postmenopausal participants with clinical, laboratory, cognitive exams, and volumetric MRI scans. Half of the cohort completed 11C-Pittsburgh Compound B (PiB) amyloid-β (Aβ) PET scans. Associations between serum FSH, LH and biomarkers were examined using voxel-based analysis, overall and stratified by menopause status. Associations with region-of-interest (ROI) hippocampal volume, plasma estradiol levels, APOE-4 status, and cognition were assessed in sensitivity analyses.ResultsFSH levels were positively associated with Aβ load in frontal cortex (multivariable adjusted P ≤ 0.05, corrected for family wise type error, FWE), an effect that was driven by the postmenopausal group (multivariable adjusted PFWE ≤ 0.044). LH levels were also associated with Aβ load in frontal cortex, which did not survive multivariable adjustment. FSH and LH were negatively associated with gray matter volume (GMV) in frontal cortex, overall and in each menopausal group (multivariable adjusted PFWE ≤ 0.040), and FSH was marginally associated with ROI hippocampal volume (multivariable adjusted P = 0.058). Associations were independent of age, clinical confounders, menopause type, hormone therapy status, history of depression, APOE-4 status, and regional effects of estradiol. There were no significant associations with cognitive scores.DiscussionIncreasing serum gonadotropin levels, especially FSH, are associated with higher Aβ load and lower GMV in some AD-vulnerable regions of midlife women at risk for AD. These findings are consistent with preclinical work and provide exploratory hormonal targets for precision medicine strategies for AD risk reduction.

The specialised regional functionality of the mature human cortex partly emerges through experience-dependent specialisation during early development. Our existing understanding of functional specialisation in the infant brain is based on evidence from unitary imaging modalities and has thus focused on isolated estimates of spatial or temporal selectivity of neural or haemodynamic activation, giving an incomplete picture. We speculate that functional specialisation will be underpinned by better coordinated haemodynamic and metabolic changes in a broadly orchestrated physiological response. To enable researchers to track this process through development, we develop new tools that allow the simultaneous measurement of coordinated neural activity (EEG), metabolic rate, and oxygenated blood supply (broadband near-infrared spectroscopy) in the awake infant. In 4- to 7-month-old infants, we use these new tools to show that social processing is accompanied by spatially and temporally specific increases in coupled activation in the temporal-parietal junction, a core hub region of the adult social brain. During non-social processing, coupled activation decreased in the same region, indicating specificity to social processing. Coupling was strongest with high-frequency brain activity (beta and gamma), consistent with the greater energetic requirements and more localised action of high-frequency brain activity. The development of simultaneous multimodal neural measures will enable future researchers to open new vistas in understanding functional specialisation of the brain.

Abstract Background and Aims Data on nonfatal suicide attempts in Germany are sparse. The study aimed to analyze data on nonfatal suicide attempts and consecutive diagnostic steps to identify secondary injuries after strangulation. Methods All admissions after nonfatal suicide attempt in a large Bavarian psychiatric hospital between 2014 and 2018 were reviewed and the methods were analyzed. Results A total of 2125 verified cases out of 2801 registered cases of nonfatal suicide attempts were included in further analysis. The most common methods were intoxication (n = 1101, 51.8%), cutting (n = 461, 21.7%), and strangulation (n = 183, 8.6%). Among survivors of strangulation with external neck compression (n = 99, 54.1%), no diagnostic steps were performed in 36 (36.4%) patients and insufficient imaging in 13 (20.6%) patients. Carotid artery dissection was detected in two (4.0%) of 50 patients with adequate neuroimaging. Conclusions This study provides details on nonfatal suicide attempts in Germany. Slightly more than half of the patients with strangulation underwent adequate diagnostic work‐up, with 4.0% being diagnosed with dissection. Further studies with systematic screening for dissection after strangulation in psychiatric hospitals are recommended to reduce possible under‐reporting.

On behalf of the BraYn Association Ets, we are pleased to present the Abstracts of the Fifth Brainstorming Research Assembly for Young Neuroscientists, which was held in Rome, Italy from 28–30 September 2022. We congratulate all the presenters on their research work and contribution.

Abstract Background In December 2019, coronavirus disease 2019 spread worldwide, causing acute respiratory distress syndrome. Coronavirus disease 2019 presents from an asymptomatic infection to severe disease causing multiorgan failure. Neurological manifestations were observed in some patients, including intracerebral hemorrhage. Bilateral basal ganglia hemorrhage is rare due to trauma. Case presentation Our patient was a 14-year-old Iranian boy with multiple trauma and loss of consciousness who tested positive for coronavirus disease 2019. The brain computed tomography scan reported bilateral basal ganglia hemorrhage. Bilateral ground glass opacity was reported through a chest computed tomography scan. Discussion and conclusions In this study, we reported a 14-year-old boy referred to the emergency room due to multiple trauma. Through the medical interventions, bilateral basal ganglia hemorrhage was discovered incidentally. Coronavirus disease 2019 was detected in this patient on the basis of findings in chest computed tomography scan and positive real reverse transcription polymerase chain reaction test. Several clinical reports and series exploring the relationship between coronavirus disease 2019 and ischemic strokes have been published. Coronavirus disease 2019, like other acute respiratory syndromes, can invade the central nervous system through hematogenous and neuronal dissemination or it can be an immune response to the cytokine storm. In conclusion, it is vital to know the pathophysiology of the neurological manifestations of coronavirus disease 2019 and prevent the mild neurological manifestations leading to severe conditions.