Cognitive challenges and brain structure variations are common in autism spectrum disorder (ASD) but are rarely explored in middle-to-old aged autistic adults. Cognitive deficits that overlap between young autistic individuals and elderlies with dementia raise an important question: does compromised cognitive ability and brain structure during early development drive autistic adults to be more vulnerable to pathological aging conditions, or does it protect them from further decline? To answer this question, we have synthesized current theoretical models of aging in ASD and conducted a systematic literature review (Jan 1, 1980 - Feb 29, 2024) and meta-analysis to summarize empirical studies on cognitive and brain deviations in middle-to-old aged autistic adults. We explored findings that support different aging theories in ASD and addressed study limitations and future directions. This review sheds light on the poorly understood consequences of aging question raised by the autism community to pave the way for future studies to identify sensitive and reliable measures that best predict the onset, progression, and prognosis of pathological aging in ASD.

OBJECTIVE: The purpose of this study was to describe intellectual disability and its association with epilepsy and brain imaging, in a population-based group of children with hemiplegic (unilateral) cerebral palsy, previously investigated and published in 2020.
METHODS: Forty-seven children of school age in northern Stockholm, fulfilling the Surveillance of Cerebral Palsy in Europe-criteria of hemiplegic (unilateral spastic) cerebral palsy, were invited to participate in the study. Twenty-one children consented to participate. A WISC (Wechsler Intelligence Scale for Children)-test was performed by an experienced psychologist.
RESULTS: In the study population of twenty-one children, 57 % (n 12) displayed uneven cognitive profiles, 38 % (n 8) intellectual disability and 62 % (n 13) had a normal IQ. 43 % (n 9) developed epilepsy. Children with extensive brain lesions had more severe intellectual disability.
CONCLUSIONS: In this study intellectual disability and/or epilepsy were associated with the type and extent of the underlying brain lesion. Intellectual disability and uneven cognitive profiles were common. We therefore recommend individual cognitive assessment to ensure an optimal school start.

Internet Gaming Disorder (IGD) is an emerging public health concern; effective treatments are still under development. This mini-review focuses on summarizing the main scientific evidence from psychological, pharmacological, brain imaging, and emerging treatment approaches for IGD. We searched PubMed and Scopus databases using keywords related to IGD and treatment. Cognitive behavioral therapy (CBT) is the most extensively researched psychological treatment for IGD, supported by several randomized controlled trials (RCTs). Other promising approaches include mindfulness, relapse prevention, abstinence protocols, and family therapy. Pharmacological treatments like bupropion and escitalopram have shown benefits, especially when IGD is comorbid with conditions like major depressive disorder. However, the quality of evidence is moderate for psychological interventions but low to moderate for pharmacological approaches. Emerging treatments such as transcranial direct current stimulation (tDCS), repetitive transcranial magnetic stimulation (rTMS), and electro-acupuncture have demonstrated efficacy in reducing IGD symptoms and modulating brain activity. Brain imaging techniques like functional magnetic resonance imaging (fMRI) have provided insights into the neural mechanisms underlying IGD and treatment effects, although these studies lack randomized controlled designs. While multimodal approaches show promise, larger, well-designed RCTs are needed to establish effective IGD treatments.

Gender inequality has been a long-standing issue throughout history, with limited progress despite the rise of women in the workforce. Historically, women were deemed inferior to men, including within the medical profession, due to perceived bodily differences. This perception was reinforced in religious texts, depicting women as bearing the burden of the first woman\'s transgressions. Such attitudes also influenced the treatment of women\'s health, with menstruation viewed as a natural source of suffering. Nevertheless, a thorough examination of medical history unveils a deep-rooted bias against women.This antiquated and discriminatory notion lacks any foundation in scientific truth. Indeed, an examination of the contributions made by female physicians reveals that they deliver equivalent levels of care, attentiveness, preventive measures, and therapeutic efficacy as their male counterparts (1-2). The narratives of female trailblazers in the medical field, like Alice Rosenstein, the first female neurosurgeon in Germany, provide compelling evidence of this phenomenon. This paper delves into her professional journey and the significant influence she has had on the field of neurosurgery.

The prenatal and neonatal periods are two of the most important developmental stages of the human brain. It is therefore crucial to understand normal brain development and how early connections are established during these periods, in order to advance the state of knowledge on altered brain development and eventually identify early brain markers of neurodevelopmental disorders and diseases. In this systematic review (Prospero ID: CRD42024511365), we compiled resting state functional magnetic resonance imaging (fMRI) studies in healthy fetuses and neonates, in order to outline the main characteristics of typical development of the functional brain connectivity during the prenatal and neonatal periods. A systematic search of five databases identified a total of 12 573 articles. Of those, 28 articles met pre-established selection criteria based determined by the authors after surveying and compiling the major limitations reported within the literature. Inclusion criteria were: (1) resting state studies; (2) presentation of original results; (3) use of fMRI with minimum one Tesla; (4) a population ranging from 20 weeks of GA to term birth (around 37-42 weeks of PMA); (5) singleton pregnancy with normal development (absence of any complications known to alter brain development). Exclusion criteria were: (1) preterm studies; (2) post-mortem studies; (3) clinical or pathological studies; (4) twin studies; (5) papers with a sole focus on methodology (i.e. focused on tool and analysis development); (6) volumetric studies; (7) activation map studies; (8) cortical analysis studies; (9) conference papers. A risk of bias assessment was also done to evaluate each article\'s methodological rigor. 1877 participants were included across all the reviewed articles. Results consistently revealed a developmental gradient of increasing functional brain connectivity from posterior to anterior regions and from proximal-to-distal regions. A decrease in local small-world organization shortly after birth was also observed; small-world characteristics were present in fetuses and newborns, but appeared weaker in the latter group. Also, the posterior-to-anterior gradient could be associated with earlier development of the sensorimotor networks in the posterior regions while more complex higher-order networks (e.g. attention-related) mature later in the anterior regions. The main limitations of this systematic review stem from the inherent limitations of functional imaging in fetuses, mainly: unevenly distributed populations and limited sample sizes; fetal movements in the womb and other imaging obstacles; and a large voxel resolution when imaging a small brain. Another limitation specific to this review is the relatively small number of included articles compared to very a large search result, which may have led to relevant articles having been overlooked.

UNASSIGNED: A growing number of advanced neuroimaging studies have compared brain structure and function in long term meditators to non-meditators. The goal is to determine if there may be long term effects on the brain from practicing meditation. In this paper, we present new data on the long term effects of a novel meditation practice in which the focus is on clitoral stimulation. The findings from such a study have implications for potential therapeutic uses with regard to various neurological or psychiatric conditions.
UNASSIGNED: We evaluated the cerebral glucose metabolism in 40 subjects with an extended history (>1 year of practice, 2-3 times per week) performing the meditation practice called Orgasmic Meditation (OM) and compared their brains to a group of non-meditating healthy controls (N = 19). Both meditation and non-meditation subjects underwent brain PET after injection with 148 to 296 MBq of FDG using a standard imaging protocol. Resting FDG PET scans of the OM group were compared to the resting scans of healthy, non-meditating, controls using statistical parametric mapping.
UNASSIGNED: The OM group showed significant differences in metabolic activity at rest compared to the controls. Specifically, there was significantly lower metabolism in select areas of the frontal, temporal, and parietal lobes, as well as the anterior cingulate, insula, and thalamus, in the OM group compared to the controls. In addition, there were notable distinctions between the males and females with the females demonstrating significantly lower metabolism in the thalamus and insula.
UNASSIGNED: Overall, these findings suggest that the long term meditation practitioners of OM have different patterns of resting brain metabolism. Since these areas of the brain in which OM practitioners differ from controls are involved in cognition, attention, and emotional regulation, such findings have implications for understanding how this meditation practice might affect practitioners over long periods of time.

Functional network (FN) analyses play a pivotal role in uncovering insights into brain function and understanding the pathophysiology of various brain disorders. This paper focuses on classical and advanced methods for deriving brain FNs from functional magnetic resonance imaging (fMRI) data. We systematically review their foundational principles, advantages, shortcomings, and interrelations, encompassing both static and dynamic FN extraction approaches. In the context of static FN extraction, we present hypothesis-driven methods such as region of interest (ROI)-based approaches as well as data-driven methods including matrix decomposition, clustering, and deep learning. For dynamic FN extraction, both window-based and windowless methods are surveyed with respect to the estimation of time-varying FN and the subsequent computation of FN states. We also discuss the scope of application of the various methods and avenues for future improvements.

BACKGROUND: Whether specific imaging aspects can be used to identify cryptogenic stroke (CS) patients with high risk of underlying atrial fibrillation (AF) remains unclear. The purpose of this study was to evaluate brain-imaging features in CS patients and their utility as AF predictors.
METHODS: The Nordic Atrial Fibrillation and Stroke study was a prospective observational study of CS and transient ischemic attack patients undergoing 12-month cardiac-rhythm monitoring, biomarker and clinical assessments. In this imaging sub-study, brain magnetic resonance imaging and computed tomography scans from 106 patients were assessed for acute and chronic ischemic lesions in relation to AF occurrence and included in a score to predict AF. Receiver operating characteristics (ROC) curve was used to evaluate the discriminative ability of the score and for its dichotomization for predictive model.
RESULTS: Age, periventricular white-matter hyperintensities (PVWMH), acute lesion size, and vessel occlusion were significantly associated with AF. Acute and chronic cortical infarcts as well as chronic cerebellar infarcts were numerically more frequent in the AF group than the non-AF group. A score consisting of six features (0-6 points) was proposed (age ≥ 65 years, chronic cortical or cerebellar lesions, acute cortical lesions, PVWMH ≥ 2 in Fazekas scale, vessel occlusion, and acute lesion size ≥ 10 mm). Area under ROC curve was 0.735 and a score of ≥ 3 points was a predictor of AF.
CONCLUSIONS: The suggested score was shown to identify CS patients with an increased risk of underlying AF.

Over the first years of life, the brain undergoes substantial organization in response to environmental stimulation. In a silent world, it may promote vision by (i) recruiting resources from the auditory cortex and (ii) making the visual cortex more efficient. It is unclear when such changes occur and how adaptive they are, questions that children with cochlear implants can help address. Here, we examined 7-18 years old children: 50 had cochlear implants, with delayed or age-appropriate language abilities, and 25 had typical hearing and language. High-density electroencephalography and functional near-infrared spectroscopy were used to evaluate cortical responses to a low-level visual task. Evidence for a \'weaker visual cortex response\' and \'less synchronized or less inhibitory activity of auditory association areas\' in the implanted children with language delays suggests that cross-modal reorganization can be maladaptive and does not necessarily strengthen the dominant visual sense.

BACKGROUND: The significance of diagnosing illnesses associated with brain cognitive and gait freezing phase patterns has led to a recent surge in interest in the study of gait for mental disorders. A more precise and effective way to characterize and classify many common gait problems, such as foot and brain pulse disorders, can improve prognosis evaluation and treatment options for Parkinson patients. Nonetheless, the primary clinical technique for assessing gait abnormalities at the moment is visual inspection, which depends on the subjectivity of the observer and can be inaccurate.
OBJECTIVE: This study investigates whether it is possible to differentiate between gait brain disorder and the typical walking pattern using machine learning driven supervised learning techniques and data obtained from inertial measurement unit sensors for brain, hip and leg rehabilitation.
METHODS: The proposed method makes use of the Daphnet freezing of Gait Data Set, consisted of 237 instances with 9 attributes. The method utilizes machine learning and feature reduction approaches in leg and hip gait recognition.
RESULTS: From the obtained results, it is concluded that among all classifiers RF achieved highest accuracy as 98.9 % and Perceptron achieved lowest i.e. 70.4 % accuracy. While utilizing LDA as feature reduction approach, KNN, RF and NB also achieved promising accuracy and F1-score in comparison with SVM and LR classifiers.
CONCLUSIONS: In order to distinguish between the different gait disorders associated with brain tissues freezing/non-freezing and normal walking gait patterns, it is shown that the integration of different machine learning algorithms offers a viable and prospective solution. This research implies the need for an impartial approach to support clinical judgment.