Neuroimaging Methods Research Articles

Role of Dietary Patterns in Older Adults with Cognitive Disorders: An Umbrella Review Utilizing Neuroimaging Biomarkers.

Various dietary patterns (DPs) may benefit or harm cognitive status through their components. Publications assessing the impact of DPs on cognitive scores using neuropsychological tests have often led to less promising results. Recently, numerous meta-analyses and systematic reviews have utilized neuroimaging to identify more subtle brain-associated alterations related to cognition. Combining neuroimaging methods with neuropsychological assessments could clarify these findings. This umbrella review was conducted to systematically explore evidence on the impact of DPs on neuroimaging biomarkers in older adults with cognitive disorders. Scientific databases, including Scopus, PubMed, and Web of Science, were comprehensively searched from the earliest available data until May 11, 2024. Out of 89 papers, 15 meta-analyses and systematic reviews were included in our umbrella review. These selected papers addressed 27 DPs and their impact on neuroimaging biomarkers. Most selected papers were of moderate quality. Studies revealed that greater adherence to the Mediterranean diet (MedDiet) correlated with increased cortical thickness, improved glucose metabolism in the brain, and reduced amyloid-beta and tau deposition, as evidenced by magnetic resonance imaging and other neuroimaging techniques. Higher adherence to healthy DPs, such as the MedDiet, reduced the risk of Alzheimer's disease and mild cognitive impairment. In contrast, Western and high glycemic diets were associated with increased cognitive decline.

Structural epilepsy: current state of the problem

This review emphasizes modern conceptions in the fundamental and clinical aspects of structural epilepsy. Highly informative neuroimaging, electrophysiological and pathomorphological diagnostic methods contribution to the study of this problem is noted. Current understanding of the pathogenesis and clinical picture of the most common types of drug resistant epilepsy are presented: hippocampal sclerosis, focal cortical dysplasia and their surgical correction methods. We considered perspective directions of fundamental clinical and experimental research, focusing on the study of epileptogenesis mechanisms and developing effective methods for prevention and treatment.

Retinotopic coding organizes the interaction between internally and externally oriented brain networks.

The human brain seamlessly integrates internally generated thoughts with incoming sensory information, yet the networks supporting internal (default network, DN) and external (dorsal attention network, dATN) processing are traditionally viewed as antagonistic. This raises a crucial question: how does the brain integrate information between these seemingly opposed systems? Here, using precision neuroimaging methods, we show that these internal/external networks are not as dissociated as traditionally thought. Using densely-sampled 7T fMRI data, we defined individualized whole-brain networks from participants at rest and calculated the retinotopic preferences of individual voxels within these networks during an visual mapping task. We show that while the overall network activity between the DN and dATN is independent at rest, considering a latent retinotopic code reveals a complex, voxel-scale interaction stratified by visual responsiveness. Specifically, the interaction between the DN and dATN at rest is structured at the voxel-level by each voxel's retinotopic preferences, such that the spontaneous activity of voxels preferring similar visual field locations is more anti-correlated than that of voxels preferring different visual field locations. Further, this retinotopic scaffold integrates with the domain-specific preferences of subregions within these networks, enabling efficient, parallel processing of retinotopic and domain-specific information. Thus, DN and dATN are not independent at rest: voxel-scale interaction between these networks preserves and encodes information in both positive and negative BOLD responses, even in the absence of visual input or task demands. These findings suggest that retinotopic coding may serve as a fundamental organizing principle for brain-wide communication, providing a new framework for understanding how the brain balances and integrates internal cognition with external perception.

A Functional Magnetic Resonance Imaging Study of Phrase Structure and Subject Island Violations.

In principle, functional neuroimaging provides uniquely informative data in addressing linguistic questions, because it can indicate distinct processes that are not apparent from behavioral data alone. This could involve adjudicating the source of unacceptability via the different patterns of elicited brain responses to different ungrammatical sentence types. However, it is difficult to interpret brain activations to syntactic violations. Such responses could reflect processes that have nothing intrinsically related to linguistic representations, such as domain-general executive function abilities. To facilitate the potential use of functional neuroimaging methods to identify the source of different syntactic violations, we conducted a functional magnetic resonance imaging experiment to identify the brain activation maps associated with two distinct syntactic violation types: phrase structure (created by inverting the order of two adjacent words within a sentence) and subject islands (created by extracting a wh-phrase out of an embedded subject). The comparison of these violations to control sentences surprisingly showed no indication of a generalized violation response, with almost completely divergent activation patterns. Phrase structure violations seemingly activated regions previously implicated in verbal working memory and structural complexity in sentence processing, whereas the subject islands appeared to activate regions previously implicated in conceptual-semantic processing, broadly defined. We review our findings in the context of previous research on syntactic and semantic violations using ERPs. Although our results suggest potentially distinct underlying mechanisms underlying phrase structure and subject island violations, our results are tentative and suggest important methodological considerations for future research in this area.

Multi-modal EEG NEO-FFI with Trained Attention Layer (MENTAL) for mental disorder prediction.

Early detection and accurate diagnosis of mental disorders is difficult due to the complexity of the diagnostic process, resulting in conditions being left undiagnosed or misdiagnosed. Previous studies have demonstrated that features of Electroencephalogram (EEG) data, such as Power Spectral Density (PSD), are useful biomarkers for indicating the onset of various mental disorders. Existing models using EEG data are typically trained to distinguish between healthy and afflicted individuals, and they are unable to distinguish between individuals with different disorders. We propose MENTAL (Multi-modal EEG NEO-FFI with Trained Attention Layer) to predict an individual's mental state using both EEG and Neo-Five Factor Inventory (NEO-FFI) personality data. We include an attention layer that captures the interactions between personality traits and PSD features, and emphasizes the important PSD features. MENTAL features a Recurrent Neural Network (RNN) to model the temporal nature of EEG data. We train our model with the Two Decades Brainclinics Research Archive for Insights in Neuroscience (TDBRAIN) dataset, which consists of 1274 healthy and psychiatric individuals including over 30 different diagnoses. MENTAL is able to achieve 93.3% accuracy when trained to classify between healthy individuals and those with ADHD. When trained to identify individuals with ADHD from among 33 disorder classes, MENTAL improves accuracy from 70.5 to 81.7%. MENTAL also demonstrates over 20% improvement in predictive accuracy when trained for MDD prediction. For the multi-class classification task of three classes, MENTAL improves accuracy by 4%, and for five classes, by nearly 8%. MENTAL is the first multi-modal model that utilizes both EEG and NEO-FFI data for the task of mental disorder prediction. We are one of the first groups to utilize TDBRAIN for automated disorder classification. MENTAL is accessible and cost-effective, as EEG is more affordable than other neuroimaging methods such as MRI, and the NEO-FFI is a self- reported survey. Our model can lead to acceptance and support for individuals living with mental health challenges and improve quality of life in our society.

Examining functional Near-Infrared Spectroscopy as a tool to study brain function in bilinguals

There is increasing evidence that using more than one languages has significant effects on brain function. These effects have been observed in the developing, adult and aging brain, and have been suggested to have implications for cognitive and brain decline in bilinguals and multilinguals. Aside from extensive investigations with behavioral methods, such effects are now commonly investigated with functional neuroimaging methods, mostly functional Magnetic Resonance Imaging (fMRI) and electroencephalography (EEG). A relatively underused method in the field is functional Near-Infrared Spectroscopy (fNIRS), which carries several advantages over more established methods, including being appropriate for use with infants and children, but also with older and diseased samples. This paper provides an overview of the method and an account of how it has been used in the field of bilingual cognition. It concludes with suggestions of how the method can be best utilized in future research, highlighting it as a method with a strong potential for updating existing theories on the effects of bilingualism on brain function.

Traumatic brain injury (TBI): Is there anything we can do? A review

Introduction Traumatic brain injury (TBI) is considered a global health crisis. It results in injury to the brain from an external impact leading to severe disability, with higher incidence statistics recently observed in young adults and elderly individuals. Purpose The review aims to analyze current research findings explaining the prevalence, incidence, and demographics of such injuries stressing the need for enhanced prevention and management techniques. The pathophysiology involves a complex cascade of biochemical and cellular events, which results in necrotic-apoptotic neuronal cell death. Method The review synthesizes current research on TBI, highlighting epidemiological data, advanced diagnostics, and effective rehabilitation strategies like physical and cognitive therapy. Findings Traumatic Brain Injury is an important public health issue with high morbidity and mortality rates causing extreme damage to a person, such as comas, catatonic states for various years, or lifelong injury or demolition. Early diagnostic imaging techniques include CT, MRI, advanced neuroimaging methods, and blood-based biomarkers. In the acute setting, initial management is directed toward stabilizing and preventing secondary injuries and early mobilization. The goals of rehabilitation are to promote the highest level of functional independence and physical function, cognitive functions, and social integration. Conclusion This review highlights that the use of new rehabilitation technologies, such as VR-based and robot-assisted therapies, might lead to improved recovery. Long-term supportive care combined with community integration is mandatory for enhancing quality of life. A systematic multidisciplinary team approach and family support are needed for traumatic brain injury survivors.

Brain-movement relationship during upper-limb functional movements in chronic post-stroke patients

BackgroundFollowing a stroke, brain activation reorganisation, movement compensatory strategies, motor performance and their evolution through rehabilitation are matters of importance for clinicians. Two non-invasive neuroimaging methods allow for recording task-related brain activation: functional near-infrared spectroscopy (fNIRS) and electroencephalography (fEEG), respectively based on hemodynamic response and neuronal electrical activity. Their simultaneous measurement during movements could allow a better spatiotemporal mapping of brain activation, and when associated to kinematic parameters could unveil underlying mechanisms of functional upper limb (UL) recovery. This study aims to depict the motor cortical activity patterns using combined fNIRS-fEEG and their relationship to motor performance and strategies during UL functional tasks in chronic post-stroke patients.MethodsTwenty-one healthy old adults and 21 chronic post-stroke patients were recruited and completed two standardised functional tasks of the UL: a paced-reaching task where they had to reach a target in front of them and a circular steering task where they had to displace a target using a hand-held stylus, as fast as possible inside a circular track projected on a computer screen. The activity of the bilateral motor cortices and motor performance were recorded simultaneously utilizing a fNIRS-fEEG and kinematics platform.Results and conclusionsKinematic analysis revealed that post-stroke patients performed worse in the circular steering task and used more trunk compensation in both tasks. Brain analysis of bilateral motor cortices revealed that stroke individuals over-activated during the paretic UL reaching task, which was associated with more trunk usage and a higher level of impairment (clinical scores). This work opens up avenues for using such combined methods to better track and understand brain-movement evolution through stroke rehabilitation.

Mechanical properties of pediatric low-grade gliomas in children with and without neurofibromatosis type 1.

Prognoses for pediatric brain tumors are suboptimal, as even in low-grade tumors, management techniques can lead to damage in the developing brain. Therefore, advanced neuroimaging methods are critical for developing optimal management plans and improving patient care. Magnetic resonance elastography (MRE) has allowed for the characterization of adult gliomas by their mechanical properties, which are uniquely sensitive to the complex interplay of cellularity, vasculature, and interstitium. However, pediatric tumors differ in behavior and cytoarchitecture, and their mechanical properties have never been assessed. Here, we conduct the first study of pediatric brain tumor mechanical properties by using MRE to measure tissue stiffness and damping ratio in low grade gliomas (LGGs). We additionally measure the mechanical properties of non-neoplastic focal abnormal signal intensities (FASIs) in children with neurofibromatosis type 1 (NF1). 23 patients age 4-17 years who had MR imaging results consistent with a primary LGG or with NF1 were included in this study. We found that pediatric gliomas are on an average 10.9% softer (p = 0.010) with a 17.3% lower (p = 0.009) viscosity than reference tissue. Softness of tumors appeared consistent across tumor subtypes and unrelated to tumor size or contrast-enhancement. In NF1 we found that, unlike gliomas, FASIs are stiffer, though not significantly, than reference tissue by an average of 10.4% and have a 16.7% lower damping ratio. Measuring tumor mechanical properties patterning and heterogeneity has potential to aid in prediction of biological behavior and inform management strategies for pediatric patients.

Synthetic data in generalizable, learning-based neuroimaging

Abstract Synthetic data has emerged as an attractive option for developing machine learning methods in human neuroimaging, particularly in magnetic resonance imaging (MRI)— a modality where image contrast depends enormously on acquisition hardware and parameters. This retrospective paper reviews a family of recently proposed methods, based on synthetic data, for generalizable machine learning in brain MRI analysis. Central to this framework is the concept of domain randomization, which involves training neural networks on a vastly diverse array of synthetically generated images with random contrast properties. This technique has enabled robust, adaptable models that are capable of handling diverse MRI contrasts, resolutions, and pathologies, while working out-of-the-box, without retraining. We have successfully applied this method to tasks such as whole brain segmentation (SynthSeg), skull-stripping (SynthStrip), registration (SynthMorph, EasyReg), super-resolution and MR contrast transfer (SynthSR). Beyond these applications, the paper discusses other possible use cases and future work in our methodology. Neural networks trained with synthetic data enable the analysis of clinical MRI, including large retrospective datasets, while greatly alleviating (and sometimes eliminating) the need for substantial labeled datasets, and offer enormous potential as robust tools to address various research goals.

Results of Using Different Brain Reperfusion Methods in Patients with Acute Ischemic Stroke

Acute ischemic stroke (AIS) occupies one of the leading places in the structure of disability and mortality in adult population throughout the world, which causes a significant social and economic burden of this pathology. Recent advances in AIS diagnosis and treatment have significantly reduced mortality and improved functional outcomes in patients. However, categories of patients with unsatisfactory results and complications of reperfusion therapy still remain. In some of them, mortality may exceed the overall rate. To this day, there are controversial aspects in determining indications and choosing the preferred method of cerebral reperfusion. The presented cases describe the results of using thrombolytic therapy and thromboextraction in AIS patients of different age groups. The importance of various neuroimaging methods (multislice computed tomography, computed angiography, duplex ultrasound of extra- and intracranial arteries) for timely diagnosis and determination of optimal revascularization tactics was noted.

An fMRI study of phrase structure and subject island violations.

In principle, functional neuroimaging provides uniquely informative data in addressing linguistic questions, because it can indicate distinct processes that are not apparent from behavioral data alone. This could involve adjudicating the source of unacceptability via the different patterns of elicited brain responses to different ungrammatical sentence types. However, it is difficult to interpret brain activations to syntactic violations. Such responses could reflect processes that have nothing intrinsically related to linguistic representations, such as domain-general executive function abilities. In order to facilitate the potential use of functional neuroimaging methods to identify the source of different syntactic violations, we conducted an fMRI experiment to identify the brain activation maps associated with two distinct syntactic violation types: phrase structure (created by inverting the order of two adjacent words within a sentence) and subject islands (created by extracting a wh-phrase out of an embedded subject). The comparison of these violations to control sentences surprisingly showed no indication of a generalized violation response, with almost completely divergent activation patterns. Phrase structure violations seemingly activated regions previously implicated in verbal working memory and structural complexity in sentence processing, whereas the subject islands appeared to activate regions previously implicated in conceptual-semantic processing, broadly defined. We review our findings in the context of previous research on syntactic and semantic violations using event-related potentials. Although our results suggest potentially distinct underlying mechanisms underlying phrase structure and subject island violations, our results are tentative and suggest important methodological considerations for future research in this area.

Iron accumulation/overload and Alzheimer's disease risk factors in the precuneus region: A comprehensive narrative review.

Alzheimer's disease (AD) is a neurodegenerative disease that is characterized by amyloid plaques, neurofibrillary tangles, and neuronal loss. Early cerebral and body iron dysregulation and accumulation interact with AD pathology, particularly in the precuneus, a crucial functional hub in cognitive functions. Quantitative susceptibility mapping (QSM), a novel post-processing approach, provides insights into tissue iron levels and cerebral oxygen metabolism and reveals abnormal iron accumulation early in AD. Increased iron deposition in the precuneus can lead to oxidative stress, neuroinflammation, and accelerated neurodegeneration. Metabolic disorders (diabetes, non-alcoholic fatty liver disease (NAFLD), and obesity), genetic factors, and small vessel pathology contribute to abnormal iron accumulation in the precuneus. Therefore, in line with the growing body of literature in the precuneus region of patients with AD, QSM as a neuroimaging method could serve as a non-invasive biomarker to track disease progression, complement other imaging modalities, and aid in early AD diagnosis and monitoring.

RATING SCALES AND INDICATORS OF DIFFUSION TENSOR IMAGING IN PREDICTING MOTOR DEFICIT REGRESSION IN PATIENTS WITH CEREBRAL STROKE

Stroke and subsequent movement disorders are a significant medical and social problem. In 2021, 500 thousand newly diagnosed strokes were registered in the Russian Federation. No more than 10 % of people return to work within the first year after a stroke, 30 % remain disabled for life. In this regard, it is the relevant to predict motor disease outcomes at different periods in patients with a cerebral stroke. The currently existing severity scales are mostly used to characterize early movement disorders, and long-term effects often remain unassessed. There are no methods for predicting the degree of movement disorders in patients with a cerebral stroke in the long term. Objectively, information on the ratio of the level of brain damage and the likelihood of subsequent motor deficit improvement in vivo can be obtained from neuroimaging images. Predicting the severity of movement disorders is potentially possible by analyzing the state of CNS conducting pathways, primarily the corticospinal tracts. This paper presents our vision on using a clinical neuroimaging method to predict the regression of motor consequences after a cerebral stroke using neurological rating scales and visual assessment of the corticospinal tracts during MRI based on the modern literature analysis. According to the literature, clinical scales used in the acute period of acute cerebrovascular accident correlate with the assessment of corticospinal tract profile. Therefore, the combination of these methods is promising while assessing motor deficit regression.

The Neuroanatomy of Induced Pluripotent Stem Cells: In Vitro Models of Subcortical Nuclei in Neurodegenerative Disorders.

Neuromodulatory subcortical systems (NSSs) are monoaminergic and cholinergic neuronal groups that are markedly and precociously involved in the pathogenesis of many neurodegenerative disorders (NDDs), including Parkinson's and Alzheimer's diseases. In humans, although many tools have been developed to infer information on these nuclei, encompassing neuroimaging and neurophysiological methods, a detailed and specific direct evaluation of their cellular features in vivo has been difficult to obtain until recent years. The development of induced pluripotent stem cell (iPSC) models has allowed research to deeply delve into the cellular and molecular biology of NSS neurons. In fact, iPSCs can be produced easily and non-invasively from patients' fibroblasts or circulating blood monocytes, by de-differentiating those cells using specific protocols, and then be re-differentiated towards neural phenotypes, which may reproduce the specific features of the correspondent brain neurons (including NSS ones) from the same patient. In this review, we summarized findings obtained in the field of NDDs using iPSCs, with the aim to understand how reliably these might represent in vitro models of NSS. We found that most of the current literature in the field of iPSCs and NSSs in NDDs has focused on midbrain dopaminergic neurons in Parkinson's disease, providing interesting results on cellular pathophysiology and even leading to the first human autologous transplantation. Differentiation protocols for noradrenergic, cholinergic, and serotoninergic neurons have also been recently defined and published. Thus, it might be expected that in the near future, this approach could extend to other NSSs and other NDDs.

Compressive Optic Neuropathy; Clinical Findings, Etiopathogenesis, Diagnosis and Treatment

Compressive optic neuropathy (COM) is a type of optic neuropathy characterized by slow and progressive vision loss, usually unilateral, resulting from the intrinsic or extrinsic compression of any part of the optic nerve. This condition may develop because of infectious, inflammatory, vascular, traumatic, and neoplastic factors. In the early diagnosis of the disease, making differential diagnoses correctly; evaluating additional hematological, biochemical, and inflammatory blood parameters; and applying orbital and neuroimaging methods when necessary are critical. In addition, coordination with other branches of science in diagnosing and treating CON is an important step in terms of disease management and the improvement of visual prognosis.

Whole-head high-density diffuse optical tomography to map infant audio-visual responses to social and non-social stimuli

Abstract Infancy is a time of rapid brain development. High-density diffuse optical tomography (HD-DOT) is an optical neuroimaging method that maps changes in cortical haemoglobin concentration, a marker of functional brain activation. Recent years have seen a huge advance in wearable hardware for HD-DOT, however previous headgear has only been capable of sampling specific areas of the cortex. In this work, we aimed to develop headgear capable of sampling across the whole infant scalp surface and to conduct a proof-of-concept demonstration of whole-head HD-DOT in infants aged 5 to 7 months. We developed a whole-head infant implementation of the high-density LUMO design developed by Gowerlabs Ltd. (UK). HD-DOT data were collected from a cohort of infants (N = 16) during the presentation of a screen-based paradigm assessing social processing. Using whole-head HD-DOT, we mapped activity across the entirety of the optically-accessible cortex which far exceeds coverage achieved by previous infant optical neuroimaging methods. We found activity in temporal regions which corroborates previous research. Further, we mapped activity in regions outside those typically sampled in infant research using social processing paradigms, finding activation in regions across the occipital, parietal, and frontal cortices as well as an apparent inverted response in sensorimotor regions. Following this proof-of-concept, we envisage that whole-head HD-DOT will be applied to map the interaction between different regions of the brain, opening new avenues to map activity in the awake infant brain to better understand the trajectory of typical and atypical neurodevelopment.

Contributions of neuroimaging in central poststroke pain: a review.

Central neuropathic poststroke pain (CNPSP) affects up to 12% of patients with stroke in general and up to 18% of patients with sensory deficits. This pain syndrome is often incapacitating and refractory to treatment. Brain computed tomography and magnetic resonance imaging (MRI) are widely used methods in the evaluation of CNPSP. The present study aims to review the role of neuroimaging methods in CNPSP. We performed a literature review of the main clinical aspects of CNPSP and the contribution of neuroimaging methods to study its pathophysiology, commonly damaged brain sites, and possible differential diagnoses. Lastly, we briefly mention how neuroimaging can contribute to the non-pharmacological CNPSP treatment. Additionally, we used a series of MRI from our institution to illustrate this review. Imaging has been used to explain CNPSP pathogenesis based on spinothalamic pathway damage and connectome dysfunction. Imaging locations associated with CNPSP include the brainstem (mainly the dorsolateral medulla), thalamus (especially the ventral posterolateral/ventral posteromedial nuclei), cortical areas such as the posterior insula and the parietal operculum, and, more recently, the thalamocortical white matter in the posterior limb of the internal capsule. Imaging also brings the prospect of helping search for new targets for non-pharmacological treatments for CNPSP. Other neuropathic pain causes identified by imaging include syringomyelia, multiple sclerosis, and herniated intervertebral disc. Imaging is a valuable tool in the complimentary evaluation of CNPSP patients in clinical and research scenarios.

Functional near-infrared spectroscopy: A novel tool for detecting consciousness after acute severe brain injury

Recent advancements in functional neuroimaging have demonstrated that some unresponsive patients in the intensive care unit retain a level of consciousness that is inconsistent with their behavioral diagnosis of awareness. Functional near-infrared spectroscopy (fNIRS) is a portable optical neuroimaging method that can be used to measure neural activity with good temporal and spatial resolution. However, the reliability of fNIRS for detecting the neural correlates of consciousness remains to be established. In a series of studies, we evaluated whether fNIRS can record sensory, perceptual, and command-driven neural processing in healthy participants and in behaviorally nonresponsive patients. At the individual healthy subject level, we demonstrate that fNIRS can detect commonly studied resting state networks, sensorimotor processing, speech-specific auditory processing, and volitional command-driven brain activity to a motor imagery task. We then tested fNIRS with three acutely brain injured patients and found that one could willfully modulate their brain activity when instructed to imagine playing a game of tennis-providing evidence of preserved consciousness despite no observable behavioral signs of awareness. The successful application of fNIRS for detecting preserved awareness among behaviorally nonresponsive patients highlights its potential as a valuable tool for uncovering hidden cognitive states in critical care settings.

Mapping neural correlates of biological motion perception in autistic children using high-density diffuse optical tomography

BackgroundAutism spectrum disorder (ASD), a neurodevelopmental disorder defined by social communication deficits plus repetitive behaviors and restricted interests, currently affects 1/36 children in the general population. Recent advances in functional brain imaging show promise to provide useful biomarkers of ASD diagnostic likelihood, behavioral trait severity, and even response to therapeutic intervention. However, current gold-standard neuroimaging methods (e.g., functional magnetic resonance imaging, fMRI) are limited in naturalistic studies of brain function underlying ASD-associated behaviors due to the constrained imaging environment. Compared to fMRI, high-density diffuse optical tomography (HD-DOT), a non-invasive and minimally constraining optical neuroimaging modality, can overcome these limitations. Herein, we aimed to establish HD-DOT to evaluate brain function in autistic and non-autistic school-age children as they performed a biological motion perception task previously shown to yield results related to both ASD diagnosis and behavioral traits.MethodsWe used HD-DOT to image brain function in 46 ASD school-age participants and 49 non-autistic individuals (NAI) as they viewed dynamic point-light displays of coherent biological and scrambled motion. We assessed group-level cortical brain function with statistical parametric mapping. Additionally, we tested for brain-behavior associations with dimensional metrics of autism traits, as measured with the Social Responsiveness Scale-2, with hierarchical regression models.ResultsWe found that NAI participants presented stronger brain activity contrast (coherent > scrambled) than ASD children in cortical regions related to visual, motor, and social processing. Additionally, regression models revealed multiple cortical regions in autistic participants where brain function is significantly associated with dimensional measures of ASD traits.LimitationsOptical imaging methods are limited in depth sensitivity and so cannot measure brain activity within deep subcortical regions. However, the field of view of this HD-DOT system includes multiple brain regions previously implicated in both task-based and task-free studies on autism.ConclusionsThis study demonstrates that HD-DOT is sensitive to brain function that both differentiates between NAI and ASD groups and correlates with dimensional measures of ASD traits. These findings establish HD-DOT as an effective tool for investigating brain function in autistic and non-autistic children. Moreover, this study established neural correlates related to biological motion perception and its association with dimensional measures of ASD traits.