Neuroimaging Methods Research Articles

Neuroimaging of new and novel Alzheimer’s Disease (AD) mice from the MODEL AD Consortium

AbstractBackgroundThe ability of preclinical neuroimaging in rodent models of Alzheimer’s Disease and Related Dementia (ADRD) to assess the progression of disease is increasingly providing novel insights into AD, like human AD imaging. Preclinical studies have the advantage of being able to directly correlate findings to the observed neuropathology. Recently, University of California Irvine and Indiana University School of Medicine along with Jackson Laboratories were awarded funding to design and phenotype relevant mouse models of late onset AD (LOAD), forming the MODEL AD consortium (https://www.model‐ad.org/). Imaging cores from these will utilize existing state of the art translationally relevant medical imaging methodologies as well as ascertain feasibility of emerging imaging methods for increased diagnostic sensitivity.MethodTwo primary imaging modalities will be utilized: high‐field MRI (9.4T and 17.6T) and PET/CT. Deep MRI phenotyping are based on the accepted human‐AD standards like ADNI (Structural T2W, FLAIR, diffusion, perfusion (PWI), susceptibility, and functional MRI and MR spectroscopy). Novel histological vessel painting combined with PWI will assess vascular modifications. PET imaging will evaluate brain metabolism (18F‐FDG), regional perfusion (64Cu‐PTSM), amyloid (18F‐AV45), tau (18F‐AV1451), neuroinflammation (18F‐CPPC and 18F‐GSK1482160), and astrogliosis (18F‐FEBU) markers in new mouse models.ResultMODEL AD consortia will utilize platform mice (Table 1) across their lifespans and effects after being fed a high fat diet. Known and emerging GWAS risk variants for AD will be added to these platform mice to better replicate human AD. Neuroimaging data will be presented from PET/CT and MRI, focusing on exemplars from relevant AD mouse models including platform mice. A long‐term goal of the MODEL‐AD Imaging cores is to co‐register MRI, PET/CT and vascular measures to concordant histopathological and spatial RNA data. The cores have developed state‐of‐the‐art analytical pipelines to process imaging data to support spatial and temporal localization of ADRD.ConclusionThe MODEL AD consortium (UCI/IUSM/Jax) will develop new and novel AD mouse models and the imaging cores will deeply phenotype these models using existing and emerging neuroimaging methods and analytic approaches, using MRI and PET/CT. All data will be made available to AD researchers via the Sage Knowledge Network (https://adknowledgeportal.synapse.org/).

Edge time series components of functional connectivity and cognitive function in Alzheimer’s disease

Understanding the interrelationships of brain function as measured by resting-state magnetic resonance imaging and neuropsychological/behavioral measures in Alzheimer’s disease is key for advancement of neuroimaging analysis methods in clinical research. The edge time-series framework recently developed in the field of network neuroscience, in combination with other network science methods, allows for investigations of brain-behavior relationships that are not possible with conventional functional connectivity methods. Data from the Indiana Alzheimer’s Disease Research Center sample (53 cognitively normal control, 47 subjective cognitive decline, 32 mild cognitive impairment, and 20 Alzheimer’s disease participants) were used to investigate relationships between functional connectivity components, each derived from a subset of time points based on co-fluctuation of regional signals, and measures of domain-specific neuropsychological functions. Multiple relationships were identified with the component approach that were not found with conventional functional connectivity. These involved attentional, limbic, frontoparietal, and default mode systems and their interactions, which were shown to couple with cognitive, executive, language, and attention neuropsychological domains. Additionally, overlapping results were obtained with two different statistical strategies (network contingency correlation analysis and network-based statistics correlation). Results demonstrate that connectivity components derived from edge time-series based on co-fluctuation reveal disease-relevant relationships not observed with conventional static functional connectivity.

Analysis of complications of neurogenic bladder dysfunction in children

Dysfunction of the lower urinary tract is a fairly common consequence of perinatal lesions of the nervous system. Dysfunction of the bladder and muscles of the urogenital diaphragm in this group of children can be accompanied by severe urodynamic disorders and, in some cases, can lead to complications such as chronic recurrent urinary tract infection, vesicoureteral reflux, nephrosclerosis, kidney atrophy, chronic renal failure. Purpose. To study uronephrological complications in children with various forms of neurogenic bladder dysfunction and suggest methods for their prevention and pathogenetic treatment. Material and methods. 67 children with neurogenic bladder dysfunction were examined. All children underwent comprehensive uronephrological and neurological diagnostics using objective neuroimaging, electrophysiological and functional research methods. Results. Complications leading to somatic health disorders in patients were diagnosed. These included chronic cystitis — in 13 (19.4%) patients, chronic pyelonephritis — in 57 (85.1%), vesicoureteral reflux — in 25 (37.3%), chronic renal failure — in 8 (11.9%) children. Therapy for neurogenic bladder dysfunction consisted of basic therapy of the central nervous system lesion, as well as symptomatic therapy aimed at normalizing the functional state of the bladder. In the vast majority of cases of neurogenic bladder dysfunction complications, their relief occurred within a year in the process of curing the underlying disease. In some cases, in children with severe neurological disorders (myelodysplasia, meningoradiculocele), along with medication and physiotherapy, surgical manipulations (periodic bladder catheterization) and surgical interventions (epicystostomy) were performed. Conclusion. In children with detrusor hyperreflexia and mild «high» lesions of the nervous system, clinical symptoms of neurogenic bladder dysfunction were usually noted, worsening only their quality of life. In children with a significant neurological deficit and a rather severe lesion of the lumbosacral spinal cord, complications developed that worsened the somatic health of this group of patients.

Clinical observation of patient with moya-moya syndrome complicated by transient ischemic attack followed by development of hemorrhagic stroke. Features of diagnosis and treatment

Moya-moya syndrome is a chronic cerebrovascular disease associated with narrowing of the arteries of the brain, manifested by headache, epileptic seizures, transient ischemic attack, leading to the development of ischemic or hemorrhagic stroke. To date, the main pathogenetic mechanisms of moya-moya syndrome have not yet been fully identified, but some studies have shown that an important component of the development of moya-moya is a genetic factor. However, if the issue of diagnosis of this syndrome can be considered solved – it is the use of neuroimaging methods (MRI, CT) with contrast of cerebral vessels, then the issue of treatment remains relevant. The article presents a clinical case of a patient with moya-moya syndrome with the transformation of an ischemic stroke into a hemorrhagic type.

Subjective states induced by intracranial electrical stimulation matches the cytoarchitectonic organization of the human insula

Functions of the human insula have been explored extensively with neuroimaging methods and intracranial electrical stimulation studies that have highlighted a functional segregation across its subregions. A recently developed cytoarchitectonic map of the human insula has also segregated this brain region into various areas. Our knowledge of the functional organization of this brain region at the level of these fine-parceled microstructural areas remains only partially understood. We address this gap of knowledge by applying a multimodal approach linking direct electrical stimulation and task-evoked intracranial EEG recordings with microstructural subdivisions of the human insular cortex. In 17 neurosurgical patients with 142 implanted electrodes, stimulation of 40 % of the sites induced a reportable change in the conscious experience of the subjects in visceral/autonomic, anxiety, taste/olfactory, pain/temperature as well as somatosensory domains. These subjective responses showed a topographical allocation to microstructural areas defined by probabilistic cytoarchitectonic parcellation maps of the human insula. We found the pain and thermal responses to be located in areas lg2/ld2, while non-painful/non-thermal somatosensory responses corresponded to area ld3 and visceroceptive responses to area Id6. Lastly, the stimulation of area Id7 in the dorsal anterior insula, failed to induce reportable changes to subjective experience even though intracranial EEG recordings from this region captured significant time-locked high-frequency activity (HFA). Our results provide a multimodal map of functional subdivisions within the human insular cortex at the individual brain basis and characterize their anatomical association with fine-grained cytoarchitectonic parcellations of this brain structure.

5 Associations Between Regional Perfusion and Locus Coeruleus MRI Contrast are Moderated by Plasma Alzheimer’s Disease Biomarkers in Older Adults

Objective:The locus coeruleus (LC) innervates the cerebrovasculature and plays a crucial role in optimal regulation of cerebral blood flow. However, no human studies to date have examined links between these systems with widely available neuroimaging methods. We quantified associations between LC structural integrity and regional cortical perfusion and probed whether varying levels of plasma Alzheimer’s disease (AD) biomarkers (Aß42/40 ratio and ptau181) moderated these relationships.Participants and Methods:64 dementia-free community-dwelling older adults (ages 55-87) recruited across two studies underwent structural and functional neuroimaging on the same MRI scanner. 3D-pCASL MRI measured regional cerebral blood flow in limbic and frontal cortical regions, while T1-FSE MRI quantified rostral LC-MRI contrast, a well-established proxy measure of LC structural integrity. A subset of participants underwent fasting blood draw to measure plasma AD biomarker concentrations (Aß42/40 ratio and ptau181). Multiple linear regression models examined associations between perfusion and LC integrity, with rostral LC-MRI contrast as predictor, regional CBF as outcome, and age and study as covariates. Moderation analyses included additional terms for plasma AD biomarker concentration and plasma x LC interaction.Results:Greater rostral LC-MRI contrast was linked to lower regional perfusion in limbic regions, such as the amygdala (ß = -0.25, p = 0.049) and entorhinal cortex (ß = -0.20, p = 0.042), but was linked to higher regional perfusion in frontal cortical regions, such as the lateral (ß = 0.28, p = 0.003) and medial (ß = 0.24, p = 0.05) orbitofrontal (OFC) cortices. Plasma amyloid levels moderated the relationship between rostral LC and amygdala CBF (Aß42/40 ratio x rostral LC interaction term ß = -0.31, p = 0.021), such that as plasma Aß42/40 ratio decreased (i.e., greater pathology), the strength of the negative relationship between rostral LC integrity and amygdala perfusion decreased. Plasma ptau181levels moderated the relationship between rostral LC and entorhinal CBF (ptau181 x rostral LC interaction term ß = 0.64, p = 0.001), such that as ptau181 increased (i.e., greater pathology), the strength of the negative relationship between rostral LC integrity and entorhinal perfusion decreased. For frontal cortical regions, ptau181 levels moderated the relationship between rostral LC and lateral OFC perfusion (ptau181 x rostral LC interaction term ß = -0.54, p = .004), as well as between rostral LC and medial OFC perfusion (ptau181 x rostral LC interaction term ß = -0.53, p = .005), such that as ptau181 increased (i.e., greater pathology), the strength of the positive relationship between rostral LC integrity and frontal perfusion decreased.Conclusions:LC integrity is linked to regional cortical perfusion in non-demented older adults, and these relationships are moderated by plasma AD biomarker concentrations. Variable directionality of the associations between the LC and frontal versus limbic perfusion, as well as the differential moderating effects of plasma AD biomarkers, may signify a compensatory mechanism and a shifting pattern of hyperemia in the presence of aggregating AD pathology. Linking LC integrity and cerebrovascular regulation may represent an important understudied pathway of dementia risk and may help to bridge competing theories of dementia progression in preclinical AD studies.

65 Neuroscience in the Everyday World: Lateralization of Brain Activity During Dual-Task Walking

Objective:Functional near-infrared spectroscopy (fNIRS) is a non-invasive functional neuroimaging method that takes advantage of the optical properties of hemoglobin to provide an indirect measure of brain activation via task-related relative changes in oxygenated hemoglobin (HbO). Its advantage over fMRI is that fNIRS is portable and can be used while walking and talking. In this study, we used fNIRS to measure brain activity in prefrontal and motor region of interests (ROIs) during single- and dual-task walking, with the goal of identifying neural correlates.Participants and Methods:Nineteen healthy young adults [mean age=25.4 (SD=4.6) years; 14 female] engaged in five tasks: standing single-task cognition (serial-3 subtraction); single-task walking at a self-selected comfortable speed on a 24.5m oval-shaped course (overground walking) and on a treadmill; and dual-task cognition+walking on the same overground course and treadmill (8 trials/condition: 20 seconds standing rest, 30 seconds task). Performance on the cognitive task was quantified as the number of correct subtractions, number of incorrect subtractions, number of self-corrected errors, and percent accuracy over the 8 trials. Walking speed (m/sec) was recorded for all walking conditions. fNIRS data were collected on a system consisting of 16 sources, 15 detectors, and 8 short-separation detectors in the following ROIs: right and left lateral frontal (RLF, LLF), right and left medial frontal (RMF, LMF), right and left medial superior frontal (RMSF, LMSF), and right and left motor (RM, LM). Lateral and medial refer to ROIs’ relative positions on lateral prefrontal cortex. fNIRS data were analyzed in Homer3 using a spline motion correction and the iterative weighted least squares method in the general linear model. Correlations between the cognitive/speed variables and ROI HbO data were applied using a Bonferroni adjustment for multiple comparisons.Results:Subjects with missing cognitive data were excluded from analyses, resulting in sample sizes of 18 for the single-task cognition, dual-task overground walking, and dual-task treadmill walking conditions. During dual-task overground walking, there was a significant positive correlation between walking speed and relative change in HbO in RMSF [r(18)=.51, p<.05] and RM [r(18)=.53, p<.05)]. There was a significant negative correlation between total number of correct subtractions and relative change in HbO in LMSF ([r(18)=-.75, p<.05] and LM [r(18)=-.52, p<.05] during dual-task overground walking. No other significant correlations were identified.Conclusions:These results indicate that there is lateralization of the cognitive and motor components of overground dual-task walking. The right hemisphere appears to be more active the faster people walk during the dual-task. By contrast, the left hemisphere appears to be less active when people are working faster on the cognitive task (i.e., serial-3 subtraction). The latter results suggest that automaticity of the cognitive task (i.e., more total correct subtractions) is related to decreased brain activity in the left hemisphere. Future research will investigate whether there is a change in cognitive automaticity over trials and if there are changes in lateralization patterns in neurodegenerative disorders that are known to differentially affect the hemispheres (e.g., Parkinson’s disease).

Advancements in computer-assisted diagnosis of Alzheimer's disease: A comprehensive survey of neuroimaging methods and AI techniques for early detection.

Alzheimer's Disease (AD) is a brain disorder that causes the brain to shrink and eventually causes brain cells to die. This neurological condition progressively hampers cognitive and memory functions, along with the ability to carry out fundamental tasks over time. From the symptoms it is very difficult to detect during its early stage. It has become necessary to develop a computer assisted diagnostic models for the early AD detection. This survey work, discussed about a review of 110 published AD detection methods and techniques from the year 2011 to till-date. This study lies in its comprehensive exploration of AD detection methods using a range of artificial intelligence (AI) techniques and neuroimaging modalities. By collecting and analysing 50 papers related to AD diagnosis datasets, the study provides a comprehensive understanding of the diversity of input types, subjects, and classes used in AD research. Summarizing 60 papers on methodologies gives researchers a succinct overview of various approaches that contribute to enhancing detection accuracy. From the review, data are acquired and pre-processed form multiple modalities of neuroimaging. This paper mainly focused on review of different datasets used, various feature extraction methods, parameters used in neuro images. To diagnosis the Alzheimer's disease, the existing methods utilized three most common artificial intelligence techniques such as machine learning, deep learning, and transfer learning. We conclude this survey work by providing future perspectives for AD diagnosis at early stage.

Conscious processing of global and local auditory irregularities causes differentiated heartbeat-evoked responses.

Recent research suggests that brain-heart interactions are associated with perceptual and self-consciousness. In this line, the neural responses to visceral inputs have been hypothesized to play a leading role in shaping our subjective experience. This study aims to investigate whether the contextual processing of auditory irregularities modulates both direct neuronal responses to the auditory stimuli (ERPs) and the neural responses to heartbeats, as measured with heartbeat-evoked responses (HERs). HERs were computed in patients with disorders of consciousness, diagnosed with a minimally conscious state or unresponsive wakefulness syndrome. We tested whether HERs reflect conscious auditory perception, which can potentially provide additional information for the consciousness diagnosis. EEG recordings were taken during the local-global paradigm, which evaluates the capacity of a patient to detect the appearance of auditory irregularities at local (short-term) and global (long-term) levels. The results show that local and global effects produce distinct ERPs and HERs, which can help distinguish between the minimally conscious state and unresponsive wakefulness syndrome patients. Furthermore, we found that ERP and HER responses were not correlated suggesting that independent neuronal mechanisms are behind them. These findings suggest that HER modulations in response to auditory irregularities, especially local irregularities, may be used as a novel neural marker of consciousness and may aid in the bedside diagnosis of disorders of consciousness with a more cost-effective option than neuroimaging methods.

Within-Individual BOLD Signal Variability and its Implications for Task-Based Cognition: A Systematic Review.

Within-individual blood oxygen level-dependent (BOLD) signal variability, intrinsic moment-to-moment signal fluctuations within a single individual in specific voxels across a given time course, is a relatively new metric recognized in the neuroimaging literature. Within-individual BOLD signal variability has been postulated to provide information beyond that provided by mean-based analysis. Synthesis of the literature using within-individual BOLD signal variability methodology to examine various cognitive domains is needed to understand how intrinsic signal fluctuations contribute to optimal performance. This systematic review summarizes and integrates this literature to assess task-based cognitive performance in healthy groups and few clinical groups. Included papers were published through October 17, 2022. Searches were conducted on PubMed and APA PsycInfo. Studies eligible for inclusion used within-individual BOLD signal variability methodology to examine BOLD signal fluctuations during task-based functional magnetic resonance imaging (fMRI) and/or examined relationships between task-based BOLD signal variability and out-of-scanner behavioral measure performance, were in English, and were empirical research studies. Data from each of the included 19 studies were extracted and study quality was systematically assessed. Results suggest that variability patterns for different cognitive domains across the lifespan (ages 7-85) may depend on task demands, measures, variability quantification method used, and age. As neuroimaging methods explore individual-level contributions to cognition, within-individual BOLD signal variability may be a meaningful metric that can inform understanding of neurocognitive performance. Further research in understudied domains/populations, and with consistent quantification methods/cognitive measures, will help conceptualize how intrinsic BOLD variability impacts cognitive abilities in healthy and clinical groups.

Pain regulation and research: Decoding the brain’s response to pain

Pain regulation and research: Decoding the brain’s response to pain Professor Patrick Stroman from the Centre for Neuroscience Studies at Queen’s University shares insights into his research on the neural basis of human pain and pain regulation, which is supported by functional magnetic resonance imaging. The research being carried out by Dr Patrick Stroman at Queen’s University is focused on understanding the neural basis of human pain and how it is altered in chronic pain conditions. Carrying out this research in humans requires the use of a non-invasive neuroimaging method such as functional magnetic resonance imaging (fMRI). Over two decades, he has developed the necessary methods and has applied them to obtain new insights into neural signaling involved with pain.

Habits and Persistent Food Restriction in Patients with Anorexia Nervosa: A Scoping Review.

The aetiology of anorexia nervosa (AN) presents a puzzle for researchers. Recent research has sought to understand the behavioural and neural mechanisms of these patients' persistent choice of calorie restriction. This scoping review aims to map the literature on the contribution of habit-based learning to food restriction in AN. PRISMA-ScR guidelines were adopted. The search strategy was applied to seven databases and to grey literature. A total of 35 studies were included in this review. The results indicate that the habit-based learning model has gained substantial attention in current research, employing neuroimaging methods, scales, and behavioural techniques. Food choices were strongly associated with dorsal striatum activity, and habitual food restriction based on the self-report restriction index was associated with clinical impairment in people chronically ill with restricting AN. High-frequency repetitive transcranial magnetic stimulation (HF-rTMS) and Regulating Emotions and Changing Habits (REaCH) have emerged as potential treatments. Future research should employ longitudinal studies to investigate the time required for habit-based learning and analyse how developmental status, such as adolescence, influences the role of habits in the progression and severity of diet-related illnesses. Ultimately, seeking effective strategies to modify persistent dietary restrictions controlled by habits remains essential.

Abstract vs Concrete: In Search for Neurocognitive Justification of the Linguistic Dichotomy

The article presents a detailed review of the studies by foreign authors on the subject of the neurophysiological substrate of abstract words. It analyses the main differences between abstract and concrete words, and the ways in which they are categorized. The article also investigates the notion of semantic association of abstract concepts. Semantic association, or associated words, are the words whose meanings are not synonymous, but which are often linked together in the real world or context. The study of memory representations that support the use of abstract knowledge in context shows significant deviation from the previous studies focusing on linguistic or other processing that occurs quickly and similarly for many different abstract concepts. Much attention is currently being paid to the research of the deeper, context-based processing of abstract words, which is central to understanding of human thought, reasoning, and decision-making processes. An extensive foreign research program over the past two decades has focused on the role of the brain’s modal sensory, motor, and affective systems in the storage and retrieval of conceptual knowledge. The article considers the works by foreign authors who conducted their research based on neuroimaging methods, experiments in free reproduction, reproduction on command, pair-associated recognition, and laboratory studies. As the result, conclusions were drawn regarding clear criteria for distinguishing between abstract and concrete concepts.

Explainable Vision Transformer with Self-Supervised Learning to Predict Alzheimer's Disease Progression Using 18F-FDG PET.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that affects millions of people worldwide. Early and accurate prediction of AD progression is crucial for early intervention and personalized treatment planning. Although AD does not yet have a reliable therapy, several medications help slow down the disease's progression. However, more study is still needed to develop reliable methods for detecting AD and its phases. In the recent past, biomarkers associated with AD have been identified using neuroimaging methods. To uncover biomarkers, deep learning techniques have quickly emerged as a crucial methodology. A functional molecular imaging technique known as fluorodeoxyglucose positron emission tomography (18F-FDG-PET) has been shown to be effective in assisting researchers in understanding the morphological and neurological alterations to the brain associated with AD. Convolutional neural networks (CNNs) have also long dominated the field of AD progression and have been the subject of substantial research, while more recent approaches like vision transformers (ViT) have not yet been fully investigated. In this paper, we present a self-supervised learning (SSL) method to automatically acquire meaningful AD characteristics using the ViT architecture by pretraining the feature extractor using the self-distillation with no labels (DINO) and extreme learning machine (ELM) as classifier models. In this work, we examined a technique for predicting mild cognitive impairment (MCI) to AD utilizing an SSL model which learns powerful representations from unlabeled 18F-FDG PET images, thus reducing the need for large-labeled datasets. In comparison to several earlier approaches, our strategy showed state-of-the-art classification performance in terms of accuracy (92.31%), specificity (90.21%), and sensitivity (95.50%). Then, to make the suggested model easier to understand, we highlighted the brain regions that significantly influence the prediction of MCI development. Our methods offer a precise and efficient strategy for predicting the transition from MCI to AD. In conclusion, this research presents a novel Explainable SSL-ViT model that can accurately predict AD progress based on 18F-FDG PET scans. SSL, attention, and ELM mechanisms are integrated into the model to make it more predictive and interpretable. Future research will enable the development of viable treatments for neurodegenerative disorders by combining brain areas contributing to projection with observed anatomical traits.

Time-series visual explainability for Alzheimer’s disease progression detection for smart healthcare

Artificial intelligence (AI)-based diagnostic systems provide less error-prone and safer support to clinicians, enhancing the medical decision-making process. This study presents a smart and reliable healthcare framework for detecting Alzheimer's disease (AD) progression. Early detection of AD before the onset of clinical symptoms is the most crucial step in starting timely treatment. To predict the conversion of cognitively normal patients to those with AD, three-dimensional (3D) magnetic resonance imaging (MRI) whole-brain neuroimaging methods have been extensively studied. However, depending on the 3D volume, this method is computationally expensive. To solve this problem, we used an approximate rank pooling method originally designed for video action recognition with a 3D MRI volume to obtain a compressed representation of multiple two-dimensional (2D) MRI slices. This study proposes a hybrid multimodal CNN-Bi-LSTM deep model for AD progression detection, in which the resulting dynamic 2D images are fused with cognitive features. Moreover, a novel explainable AI approach is proposed to provide visual explanations using the resulting longitudinal 2D dynamic images. Temporal explanations were provided by visualizing the affected brain regions captured using longitudinal 2D MRIs. By utilizing a sample of 1,692 subjects with multimodal data from the Alzheimer’s Disease Neuroimaging Initiative dataset, our method was assessed using a 10-fold cross-validation process. The model achieved an area under the receiver operating characteristics curve (AUC) of 94% using longitudinal 2D three-time-step dynamic image data. The fusion of 2D dynamic images with cognitive features enhanced the performance by 2% in terms of the AUC. Patients who gradually develop AD, show changes in various brain regions. For such patients, our system highlights the critical role of the hippocampus, medial amygdala, caudal hippocampus, and lateral amygdala at the initial time steps. In the late stages of AD, the system detects abnormalities in extra brain regions such as the medial temporal gyrus, superior temporal gyrus, fusiform gyrus, and caudal hippocampus; indicating that patients have completely progressed to AD.

Dissociation of Centrally and Peripherally Induced Transcranial Magnetic Stimulation Effects in Nonhuman Primates.

Transcranial magnetic stimulation (TMS) is a noninvasive brain stimulation method that is rapidly growing in popularity for studying causal brain-behavior relationships. However, its dose-dependent centrally induced neural mechanisms and peripherally induced sensory costimulation effects remain debated. Understanding how TMS stimulation parameters affect brain responses is vital for the rational design of TMS protocols. Studying these mechanisms in humans is challenging because of the limited spatiotemporal resolution of available noninvasive neuroimaging methods. Here, we leverage invasive recordings of local field potentials in a male and a female nonhuman primate (rhesus macaque) to study TMS mesoscale responses. We demonstrate that early TMS-evoked potentials show a sigmoidal dose-response curve with stimulation intensity. We further show that stimulation responses are spatially specific. We use several control conditions to dissociate centrally induced neural responses from auditory and somatosensory coactivation. These results provide crucial evidence regarding TMS neural effects at the brain circuit level. Our findings are highly relevant for interpreting human TMS studies and biomarker developments for TMS target engagement in clinical applications.SIGNIFICANCE STATEMENT Transcranial magnetic stimulation (TMS) is a widely used noninvasive brain stimulation method to stimulate the human brain. To advance its utility for clinical applications, a clear understanding of its underlying physiological mechanisms is crucial. Here, we perform invasive electrophysiological recordings in the nonhuman primate brain during TMS, achieving a spatiotemporal precision not available in human EEG experiments. We find that evoked potentials are dose dependent and spatially specific, and can be separated from peripheral stimulation effects. This means that TMS-evoked responses can indicate a direct physiological stimulation response. Our work has important implications for the interpretation of human TMS-EEG recordings and biomarker development.

Non-Invasive Mapping of the Neuronal Networks of Language.

This review consists of three main sections. In the first, the Introduction, the main theories of the neuronal mediation of linguistic operations, derived mostly from studies of the effects of focal lesions on linguistic performance, are summarized. These models furnish the conceptual framework on which the design of subsequent functional neuroimaging investigations is based. In the second section, the methods of functional neuroimaging, especially those of functional Magnetic Resonance Imaging (fMRI) and of Magnetoencephalography (MEG), are detailed along with the specific activation tasks employed in presurgical functional mapping. The reliability of these non-invasive methods and their validity, judged against the results of the invasive methods, namely, the "Wada" procedure and Cortical Stimulation Mapping (CSM), is assessed and their use in presurgical mapping is justified. In the third and final section, the applications of fMRI and MEG in basic research are surveyed in the following six sub-sections, each dealing with the assessment of the neuronal networks for (1) the acoustic and phonological, (2) for semantic, (3) for syntactic, (4) for prosodic operations, (5) for sign language and (6) for the operations of reading and the mechanisms of dyslexia.

Assessing non-right-handedness and atypical cerebral lateralisation as predictors of paediatric mental health difficulties.

Research utilising handedness as a proxy for atypical language lateralisation has invoked the latter to explain increased mental health difficulties in left-/mixed-handed children. The current study investigated unique associations between handedness and language lateralisation, handedness and mental health, and language lateralisation and mental health, in children, to elucidate the role of cerebral lateralisation in paediatric mental health. Participants were N = 64 (34 females [52%]; MAge = 8.56 years; SDAge = 1.33; aged 6-12 years) typically developing children. Hand preference was assessed via a reaching task, language lateralisation was assessed using functional transcranial Doppler ultrasonography (fTCD) during an expressive language task, and mental health was assessed with the Strengths and Difficulties Questionnaire. As hypothesised, leftward hand preference predicted increased general mental health issues in children, with a strong relationship noted between leftward hand preference and the emotional symptoms subscale. Contrary to expectation, no relationship was found between direction of language lateralisation and general mental health issues, although exploratory analyses of subscales showed rightward lateralisation to predict conduct problems. Hand preference and direction of language lateralisation were also not significantly associated. The relatively weak relationship between manual and language laterality coupled with discrepancy regarding the predictive scope of each phenotype (i.e., hand preference predicts overall mental health, whereas language laterality predicts only conduct problems) suggests independent developmental pathways for these phenotypes. The role of manual laterality in paediatric mental health warrants further investigation utilising a neuroimaging method with higher spatial resolution.

Abnormalities in EEG as alzheimer marker

Alzheimer’s Disease (AD) is a prevalent neurodegenerative disorder characterized by protein accumulation, cognitive decline, and irreversible brain damage. With the aging population, AD’s prevalence is rising, necessitating effective diagnostic tools. Electroencephalography (EEG), a non-invasive neuroimaging method, has emerged as a promising tool for early AD detection.

B - 01 Children with ADHD Demonstrate Cortical Thinning in Multiple Regions Compared to TD Controls.

Children with Attention-Deficit/Hyperactivity Disorder (ADHD) often demonstrate functional and volumetric differences compared to typically developing (TD) children. These regions are associated with the deficits commonly seen in ADHD. However, there is limited research on cortical thickness in ADHD, and what does exist is inconsistent in its findings. Thus, the objective of this study was to examine potential differences in cortical thickness between children with ADHD and TD controls. Participants included 73 children (aged 8-12years; 50.7% boys, 87.7% White) with ADHD and typically developing controls. It was a community sample. The children completed an MRI scan and a neuropsychological battery as part of a larger, grant-funded project (R03HD048752, R15HD065627). Group differences in cortical thickness were determined using CAT12 software. A t-test model was used for voxel-wise comparisons of cortical thickness between children with ADHD and TD controls. After aligning the MRI scans, the CAT12 software provides clusters of regions in which cortical thickness differs significantly between groups. Compared with TD controls, children with ADHD demonstrated reduced cortical thickness in three clusters in the left hemisphere and one large cluster in the right hemisphere (p < 0.05; FWE corrected). The clusters included several frontal and parietal regions, along with occipital and temporal regions. Results are consistent with findings from prior research comparing children with ADHD and TD controls using other neuroimaging methods, which displayed involvement of frontal-parietal and/or frontal-temporal networks. Furthermore, these regions are associated with cognitive and behavioral deficits commonly seen in ADHD, including various aspects of executive dysfunction.