fMRI Brain Research Articles

Brain neurovascular coupling in amyotrophic lateral sclerosis: Correlations with disease progression and cognitive impairment

AbstractBackground and purpose‘Neurovascular coupling’ (NVC) alterations, assessing the interplay between local cerebral perfusion and neural activity within a given brain region or network, may reflect neurovascular unit impairment in amyotrophic lateral sclerosis (ALS). The aim was to explore NVC as a correlation between the functional connectivity and cerebral blood flow within the large‐scale resting‐state functional magnetic resonance imaging brain networks in a sample of ALS patients compared to healthy controls (HCs).MethodsForty‐eight ALS patients (30 males; mean age 60.64 ± 9.62 years) and 32 HC subjects (14 males; mean age 55.06 ± 16 years) were enrolled and underwent 3 T magnetic resonance imaging. ALS patients were screened by clinical and neuropsychological scales and were retrospectively classified as very fast progressors (VFPs), fast progressors and slow progressors (SPs).ResultsNeurovascular coupling reduction within the default mode network (DMN) (p = 0.005) was revealed in ALS patients compared to HCs, observing, for this network, significant NVC differences between VFP and SP groups. Receiver operating characteristic curve analysis showed that impaired NVC in the DMN at baseline best discriminated VFPs and SPs (area under the curve 75%). Significant correlations were found between NVC and the executive (r = 0.40, p = 0.01), memory (r = 0.32, p = 0.04), visuospatial ability (r = 0.40, p = 0.01) and non‐ALS‐specific (r = 0.40, p = 0.01) subscores of the Edinburgh Cognitive and Behavioural ALS Screen.ConclusionsThe reduction of brain NVC in the DMN may reflect largely distributed abnormalities of the neurovascular unit. NVC alterations in the DMN could play a role in anticipating a faster clinical progression in ALS patients, aiding patient selection and monitoring during clinical trials.

Visual dysfunction of superior colliculus and lateral geniculate nucleus in idiopathic blepharospasm

BackgroundThe etiology and pathophysiology of idiopathic blepharospasm (BP) are still largely unknown. It has been hypothesized that BP is the consequence of a dysfunction of the basal ganglia loop, although cortical areas, cerebellum, and other brainstem structures may be involved. There is some evidence that the superior colliculus (SC), a sensorimotor brainstem structure, is involved in another adult-onset focal dystonia, the cervical dystonia. To date, there is no data concerning the implication of the SC in BP. ObjectivesOur study aims to investigate the role of the SC in people with idiopathic BP compared to controls using fMRI and a visual stimulation paradigm based on luminance contrast variations. MethodsPeople with idiopathic BP and controls underwent brain fMRI using a standardized protocol, allowing modulation of visual activity in the SC, the lateral geniculate nucleus (LGN), and the primary visual cortex (V1), at increasing luminance levels (1 %, 3 %, 5 %, 9 %). ResultsTen BP women and ten sex- and age-matched controls were enrolled. Compared to controls, the BP group showed no modulation of visual responses at all luminance levels (p < 0.05) in both SC and LGN. In BP, BOLD responses in V1 were significantly lower at 5 % (p = 0.001), and 9 % (p = 0.002) luminance level. ConclusionsOur findings support the concept of SC and LGN dysfunction in idiopathic BP. Brain fMRI, targeting these sub-cortical visual structures, could play a future important role both as a biomarker and in our understanding of the pathophysiology of adult-onset focal dystonias.

Functional connectivity in resting-state networks relates to short-term global cognitive functioning in cardiac arrest survivors.

Long-term cognitive impairment is common in cardiac arrest survivors. Screening to identify patients at risk is recommended. Functional magnetic resonance brain imaging (fMRI) holds potential to contribute to prediction of cognitive outcomes. In this study, we investigated the possible value of early changes in resting-state networks for predicting short and long-term cognitive functioning of cardiac arrest survivors. We performed a prospective multicenter cohort study in cardiac arrest survivors in three Dutch hospitals. Resting-state fMRI scans were acquired within a month after cardiac arrest. We primarily focused on functional connectivity within the default-mode network (DMN) and salience network (SN), and additionally explored functional connectivity in seven other networks. Cognitive outcome was measured using the Montreal Cognitive Assessment (MoCA) during hospital admission and at 3 and 12 months, and by neuropsychological examination (NPE) at 12 months. We tested mixed effects models to evaluate the value of connectivity within the networks for predicting global cognitive outcomes at the three time points, and long-term cognitive outcomes in the memory, attention, and executive functioning domains. We included 80 patients (age 60 ± 11 years, 72 (90%) male). MoCA scores increased significantly between hospital admission and 3 months (ΔMoCAhospital-3M = 2.89, p < 0.01), but not between 3 and 12 months (ΔMoCA3M-12M = 0.38, p = 0.52). Connectivity within the DMN, SN, and dorsal attention network (DAN) was positively related to global cognitive functioning during hospital admission (βDMN = 0.85, p = 0.03; βSN = 1.48, p < 0.01; βDAN = 0.96, p = 0.01), but not at 3 and 12 months. Network connectivity was also unrelated to long-term memory, attention, or executive functioning. Resting-state functional connectivity in the DMN, SN, and DAN measured in the first month after cardiac arrest is related to short-term global, but not long-term global or domain-specific cognitive performance of survivors. These results do not support the value of functional connectivity within these RSNs for prediction of long-term cognitive performance after cardiac arrest.

Magnetic resonance imaging studies on acupuncture therapy in cerebral ischemic stroke: A systematic review

Research has started using Magnetic Resonance Imaging (MRI) to elucidate the action of acupuncture on neuroplasticity following ischemic stroke (IS). Acupuncture is thought to be a potentially beneficial treatment for IS. However, the results remain inconsistent because of MRI processing techniques and study design variations. Therefore, this coordinate-based meta-analysis and systematic review aimed to assess the current state of knowledge about the regional brain fMRI imaging characteristics in acupuncture-treated IS patients. Twenty studies—including 392 IS patients and 256 health controls—met the inclusion criteria after searches via Chinese and English databases. The design techniques utilized in this research were mainly owned before-and-after controlled and randomized controlled trials. Only one study used independent component analysis (ICA), while the majority of MRI analytical techniques focus on functional connectivity (FC) and fractional amplitude of low-frequency fluctuation (fALFF/ALFF). The findings demonstrated a significant rise in the ALFF value of the left supplementary motor region after treatment with acupuncture. The left cerebellum, right inferior frontal gyrus, and hemisphere lobule VIII all showed substantial activation of Reho values. The triangular portion, BA 48, the left inferior network and inferior longitudinal fasciculus, as well as other brain areas decreased in the left inferior frontal gyrus; most research has used FC analysis employing motor areas as seed regions. We found that acupuncture regulated the motor-related network and reorganized the language-related network. Furthermore, acupuncture appears therapeutic for several IS effects, which may be connected to how acupuncture regulates the brain's plasticity.

Reproducible brain PET data analysis: easier said than done.

While a great deal of recent effort has focused on addressing a perceived reproducibility crisis within brain structural magnetic resonance imaging (MRI) and functional MRI research communities, this article argues that brain positron emission tomography (PET) research stands on even more fragile ground, lagging behind efforts to address MRI reproducibility. We begin by examining the current landscape of factors that contribute to reproducible neuroimaging data analysis, including scientific standards, analytic plan pre-registration, data and code sharing, containerized workflows, and standardized processing pipelines. We then focus on disparities in the current status of these factors between brain MRI and brain PET. To demonstrate the positive impact that further developing such reproducibility factors would have on brain PET research, we present a case study that illustrates the many challenges faced by one laboratory that attempted to reproduce a community-standard brain PET processing pipeline. We identified key areas in which the brain PET community could enhance reproducibility, including stricter reporting policies among PET dedicated journals, data repositories, containerized analysis tools, and standardized processing pipelines. Other solutions such as mandatory pre-registration, data sharing, code availability as a condition of grant funding, and online forums and standardized reporting templates, are also discussed. Bolstering these reproducibility factors within the brain PET research community has the potential to unlock the full potential of brain PET research, propelling it toward a higher-impact future.

Development and evaluation of an EMP course to teach fMRI technology and brain science in handwriting for university nursing students

In traditional nursing education, brain science is often limited to physiology, anatomy, and pathology, with little emphasis on advanced diagnostic tools like fMRI or the brain’s functional processes, such as language learning and handwriting. This study developed an English for Medical Purposes (EMP) course to teach university nursing students about fMRI technology and brain science related to handwriting, highlighting the importance of interprofessional education. The study aimed to (a) assess the course’s effectiveness in enhancing students’ knowledge in fMRI, brain science, and handwriting; and (b) evaluate the students’ satisfaction and perceptions toward the course. A single-group quasi-experimental design with pre/post-tests involved 49 nursing students. Data were collected using the Brain Science Related Knowledge Test (BSKT), assignments, and a Course Satisfaction Questionnaire (CSQ). Results showed significant improvements in students’ knowledge of fMRI and brain science in handwriting, with high assignment scores and overall course satisfaction. Students also reported increased awareness of reading professional English journals and learned about fMRI safety issues. This pioneering EMP course effectively introduced nursing students to fMRI technology and brain science in handwriting, potentially enhancing their understanding of normal brain function and its contrast with brain injury cases.

FAStEN: An Efficient Adaptive Method for Feature Selection and Estimation in High-Dimensional Functional Regressions

Functional regression analysis is an established tool for many contemporary scientific applications. Regression problems involving large and complex data sets are ubiquitous, and feature selection is crucial for avoiding overfitting and achieving accurate predictions. We propose a new, flexible and ultra-efficient approach to perform feature selection in a sparse high dimensional function-on-function regression problem, and we show how to extend it to the scalar-on-function framework. Our method, called FAStEN, combines functional data, optimization, and machine learning techniques to perform feature selection and parameter estimation simultaneously. We exploit the properties of Functional Principal Components and the sparsity inherent to the Dual Augmented Lagrangian problem to significantly reduce computational cost, and we introduce an adaptive scheme to improve selection accuracy. In addition, we derive asymptotic oracle properties, which guarantee estimation and selection consistency for the proposed FAStEN estimator. Through an extensive simulation study, we benchmark our approach to the best existing competitors and demonstrate a massive gain in terms of CPU time and selection performance, without sacrificing the quality of the coefficients’ estimation. The theoretical derivations and the simulation study provide a strong motivation for our approach. Finally, we present an application to brain fMRI data from the AOMIC PIOP1 study. Complete FAStEN code is provided at https://github.com/IBM/funGCN. Supplementary materials for this article are available online.

A comprehensive approach to lateral ventricular tumor resection: techniques, technologies, and outcomes.

This study reviews lateral ventricular tumors (LVTs), which are rare brain lesions accounting for 0.64-3.5% of brain tumors, and the unique challenges they present due to their location and growth patterns. Once deemed inoperable, advancements in microneurosurgery, imaging, and tumor pathobiology have significantly improved treatment outcomes. This letter summarizes recent studies and key findings in the management of LVTs. Research by S.A. Maryashev et al. identified risk factors for early hemorrhagic complications following the surgical resection of lateral ventricular neoplasms, highlighting the significance of patient characteristics, tumor location, and surgical approach. The study found that factors such as gender, hydrocephalus, tumor blood flow, and Evans index correlate with a higher risk of hemorrhage, with the transcallosal approach having a greater risk compared to the transcortical approach. The utilization of navigation technologies, including fMRI, neuronavigation, and intraoperative brain mapping, has been shown to reduce surgical complications and enhance patient outcomes in the treatment of lateral ventricular meningiomas. Moreover, endoscopic and endoport-assisted endoscopic techniques have proven to be valuable in intraventricular tumor surgery, enabling minimally invasive procedures with better visualization and fewer complications. The integration of advanced surgical techniques, neuroimaging, and neurophysiological monitoring emphasizes the necessity of a multidisciplinary approach to optimize patient outcomes. To improve the study's validity and applicability, further research with larger sample sizes and advanced statistical analyses is needed. This letter advocates for the continued exploration of innovative surgical techniques and technologies to enhance the management of lateral ventricular tumors.

Assessment of the Potential of Modern Diagnostic Tools in Differentiation of Minimum Conscious State from the Vegetative State

Background The problem of treatment of patients in coma and persistent disturbances of consciousness is difficult and often neglected. Purpose The study validated the effectiveness of functional magnetic resonance imaging, electroencephalography and isotopic brain perfusion in differentiating vegetative state from minimal conscious state. Methods The study was performed in a group of 61 patients with persistent disturbances of consciousness. All patients underwent CRS-R differentiation of consciousness and functional magnetic resonance imaging, EEG, and SPECT/CT studies according to a uniform diagnostic protocol. Results Analysis has shown that functional magnetic resonance imaging is an effective tool for detecting of consciousness. Analysis of the EEG showed that the occurrence of primary alpha rhythm in patients with impaired consciousness coincides with the state of minimal conscious and may indicate a favourable prognosis. Sector analysis of isotopic cerebral perfusion allowed to put forward the thesis that the key role in the maintenance of consciousness plays a medial part of the left parietal lobe. Conclusion On the basis of these results, diagnostic protocol was developed for patients with disturbances of consciousness.

A Multimodal Magnetic Resonance Imaging Study on Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Feasibility and Clinical Correlation.

Background/Objectives: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a neurological disorder characterized by post-exertional malaise. Despite its clinical relevance, the disease mechanisms of ME/CFS are not fully understood. The previous studies targeting brain function or metabolites have been inconclusive in understanding ME/CFS complexity. We combined single-voxel magnetic resonance spectroscopy (SV-MRS) and functional magnetic resonance imaging (fMRI). Our objectives were to examine the feasibility of the multimodal MRI protocol, identify possible differences between ME/CFS and healthy controls (HCs), and relate MRI findings with clinical symptoms. Methods: We enrolled 18 female ME/CFS participants (mean age: 39.7 ± 12.0 years) and five HCs (mean age: 45.6 ± 14.5 years). SV-MRS spectra were acquired from three voxels of interest: the anterior cingulate gyrus (ACC), brainstem (BS), and left dorsolateral prefrontal cortex (L-DLPFC). Whole-brain fMRI used n-back task testing working memory and executive function. The feasibility was assessed as protocol completion rate and time. Group differences in brain metabolites and fMRI activation between ME/CFS and HCs were compared and correlated with behavioral and symptom severity measurements. Results: The completion rate was 100% regardless of participant group without causing immediate fatigue. ME/CFS appeared to show a higher N-Acetylaspartate in L-DLPFC compared to HCs (OR = 8.49, p = 0.040), correlating with poorer fatigue, pain, and sleep quality scores (p's = 0.001-0.015). An increase in brain activation involving the frontal lobe and the brainstem was observed in ME/CFS compared to HCs (Z > 3.4, p's < 0.010). Conclusions: The study demonstrates the feasibility of combining MRS and fMRI to capture neurochemical and neurophysiological features of ME/CFS in female participants. Further research with larger cohorts of more representative sampling and follow-ups is needed to validate these apparent differences between ME/CFS and HCs.

Multi-Scale Spatial-Temporal Attention Networks for Functional Connectome Classification.

Many neuropsychiatric disorders are considered to be associated with abnormalities in the functional connectivity networks of the brain. The research on the classification of functional connectivity can therefore provide new perspectives for understanding the pathology of disorders and contribute to early diagnosis and treatment. Functional connectivity exhibits a nature of dynamically changing over time, however, the majority of existing methods are unable to collectively reveal the spatial topology and time-varying characteristics. Furthermore, despite the efforts of limited spatial-temporal studies to capture rich information across different spatial scales, they have not delved into the temporal characteristics among different scales. To address above issues, we propose a novel Multi-Scale Spatial-Temporal Attention Networks (MSSTAN) to exploit the multi-scale spatial-temporal information provided by functional connectome for classification. To fully extract spatial features of brain regions, we propose a Topology Enhanced Graph Transformer module to guide the attention calculations in the learning of spatial features by incorporating topology priors. A Multi-Scale Pooling Strategy is introduced to obtain representations of brain connectome at various scales. Considering the temporal dynamic characteristics between dynamic functional connectome, we employ Locality Sensitive Hashing attention to further capture long-term dependencies in time dynamics across multiple scales and reduce the computational complexity of the original attention mechanism. Experiments on three brain fMRI datasets of MDD and ASD demonstrate the superiority of our proposed approach. In addition, benefiting from the attention mechanism in Transformer, our results are interpretable, which can contribute to the discovery of biomarkers. The code is available at https://github.com/LIST-KONG/MSSTAN.

Cerebro-spinal somatotopic organization uncovered through functional connectivity mapping

Abstract Somatotopy, the topographical arrangement of sensorimotor pathways corresponding to distinct body parts, is a fundamental feature of the human central nervous system (CNS). Traditionally, investigations into brain and spinal cord somatotopy have been conducted independently, primarily utilizing body stimulations or movements. To date, however, no study has probed the somatotopic arrangement of cerebro-spinal functional connections in vivo in humans. In this study, we used simultaneous brain and cervical spinal cord functional magnetic resonance imaging (fMRI) to demonstrate how the coordinated activities of these two CNS levels at rest can reveal their shared somatotopy. Using functional connectivity analyses, we mapped preferential correlation patterns between each spinal cord segment and distinct brain regions, revealing a somatotopic gradient within the cortical sensorimotor network. We then validated this large-scale somatotopic organization through a complementary data-driven analysis, where we effectively identified spinal cord segments through the connectivity profiles of their voxels with the sensorimotor cortex. These findings underscore the potential of resting-state cerebro-spinal cord fMRI to probe the large-scale organization of the human sensorimotor system with minimal experimental burden, holding promise for gaining a more comprehensive understanding of normal and impaired somatosensory-motor functions.

Concurrent tDCS-fMRI after stroke reveals link between attention network organization and motor improvement

Restoring motor function after stroke necessitates involvement of numerous cognitive systems. However, the impact of damage to motor and cognitive network organization on recovery is not well understood. To discover correlates of successful recovery, we explored imaging characteristics in chronic stroke subjects by combining noninvasive brain stimulation and fMRI. Twenty stroke survivors (6 months or more after stroke) were randomly assigned to a single session of transcranial direct current stimulation (tDCS) or sham during image acquisition. Twenty healthy subjects were included as controls. tDCS was limited to 10 min at 2 mA to serve as a mode of network modulation rather than therapeutic delivery. Fugl-Meyer Assessments (FMA) revealed significant motor improvement in the chronic stroke group receiving active stimulation (p = 0.0005). Motor changes in this group were correlated in a data-driven fashion with imaging features, including functional connectivity (FC), surface-based morphometry, electric field modeling and network topology, focusing on relevant regions of interest. We observed stimulation-related changes in FC in supplementary motor (p = 0.0029), inferior frontal gyrus (p = 0.0058), and temporo-occipital (p = 0.0095) areas, though these were not directly related to motor improvement. The feature most strongly associated with FMA improvement in the chronic stroke cohort was graph topology of the dorsal attention network (DAN), one of the regions surveyed and one with direct connections to each of the areas with FC changes. Chronic stroke subjects with a greater degree of motor improvement had lower signal transmission cost through the DAN (p = 0.029). While the study was limited by a small stroke cohort with moderate severity and variable lesion location, these results nevertheless suggest a top-down role for higher order areas such as attention in helping to orchestrate the stroke recovery process.

Gait Analysis and Magnetic Resonance Imaging Characteristics in Patients with Isolated Rapid Eye Movement Sleep Behavior Disorder.

Isolated rapid eye movement sleep behavioral disorder (iRBD) can precede neurodegenerative diseases. There is an urgent need for biomarkers to aid early intervention and neuroprotection. The aim is to assess quantitative motor, cognitive, and brain magnetic resonance imaging (MRI) characteristics in iRBD patients. Thirty-eight polysomnography-confirmed iRBD patients and 28 age- and sex-matched healthy controls underwent clinical, cognitive, and motor functional evaluations, along with brain MRI. Motor tasks included nine-hole peg test, five-times-sit-to-stand test, timed-up-and-go test, and 4-meter walking test with and without cognitive dual task. Quantitative spatiotemporal gait parameters were obtained using an optoelectronic system. Brain MRI analysis included functional connectivity (FC) of the main resting-state networks, gray matter (GM) volume using voxel-based morphometry, cortical thickness, and deep GM and brainstem volumes using FMRIB's Integrated Registration and Segmentation Tool and FreeSurfer. iRBD patients relative to healthy subjects exhibited a poorer performance during the nine-hole peg test and five-times-sit-to-stand test, and greater asymmetry of arm-swing amplitude and stride length variability during dual-task gait. Dual task significantly worsened the walking performance of iRBD patients more than healthy controls. iRBD patients exhibited nonmotor symptoms, and memory, abstract reasoning, and visuospatial deficits. iRBD patients exhibited decreased FC of pallidum and putamen within the basal ganglia network and occipital and temporal areas within the visuo-associative network, and a reduced volume of the supramarginal gyrus. Brain functional alterations correlated with gait changes. Subtle motor and nonmotor alterations were identified in iRBD patients, alongside brain structural and functional MRI changes. These findings may represent early signs of neurodegeneration and contribute to the development of predictive models for progression to parkinsonism. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Research into super-resolution in medical imaging from 2000 to 2023: bibliometric analysis and visualization.

Super-resolution (SR) refers to the use of hardware or software methods to enhance the resolution of low-resolution (LR) images and produce high-resolution (HR) images. SR is applied frequently across a variety of medical imaging contexts, particularly in the enhancement of neuroimaging, with specific techniques including SR microscopy-used for diagnostic biomarkers-and functional magnetic resonance imaging (fMRI)-a neuroimaging method for the measurement and mapping of brain activity. This bibliometric analysis of the literature related to SR in medical imaging was conducted to identify the global trends in this field, and visualization via graphs was completed to offer insights into future research prospects. In order to perform a bibliometric analysis of the SR literature, this study sourced all publications from the Web of Science Core Collection (WoSCC) database published from January 1, 2000, to October 11, 2023. A total of 3,262 articles on SR in medical imaging were evaluated. VOSviewer was used to perform co-occurrence and co-authorship analysis, and network visualization of the literature data, including author, journal, publication year, institution, and keywords, was completed. From 2000 to 2023, the annual publication volume surged from 13 to 366. The top three journals in this field in terms of publication volume were as follows: (I) Scientific Reports (86 publications), (II) IEEE Transactions on Medical Imaging (74 publications), and (III) IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control (56 publications). The most prolific country, institution, and author were the United States (1,017 publications; 31,301 citations), the Chinese Academy of Sciences (124 publications; 2,758 citations), and Dinggang Shen (20 publications; 671 citations), respectively. A cluster analysis of the top 100 keywords was conducted, which revealed the presence of five co-occurrence clusters: (I) SR and artificial intelligence (AI) for medical image enhancement, (II) SR and inverse problem processing concepts for positron emission tomography (PET) image processing, (III) SR ultrasound through microbubbles, (IV) SR microscopy for Alzheimer and Parkinson diseases, and (V) SR in brain fMRI: rapid acquisition and precise imaging. The most recent high-frequency keywords were deep learning (DL), magnetic resonance imaging (MRI), and convolutional neural networks (CNNs). Over the past two decades, the output of publications by countries, institutions, and authors in the field of SR in medical imaging has steadily increased. Based on bibliometric analysis of international trends, the resurgence of SR in medical imaging has been facilitated by advancements in AI. The increasing need for multi-center and multi-modal medical images has further incentivized global collaboration, leading to the diverse research paths in SR medical imaging among prominent scientists.

Functional connectivity of the posterior cingulate cortex in autism spectrum disorder

The purpose of this study was to assess the functional connectivity of the posterior cingulate cortex in autism spectrum disorder (ASD). We used resting-state functional magnetic resonance imaging (rsfMRI) brain scans of adolescents diagnosed with ASD and a neurotypical control group. The Autism Brain Imaging Data Exchange (ABIDE) consortium was utilized to acquire data from the University of Michigan (145 subjects) and data from the New York University (183 subjects). The posterior cingulate cortex showed reduced connectivity with the anterior cingulate cortex for the ASD group compared to the control group. These two brain regions have previously both been linked to ASD symptomology. Specifically, the posterior cingulate cortex has been associated with behavioral control and executive functions, which appear to be responsible for the repetitive and restricted behaviors (RRB) in ASD. Our findings support previous data indicating a neurobiological basis of the disorder, and the specific functional connectivity changes involving the posterior cingulate cortex and anterior cingulate cortex may be a potential neurobiological biomarker for the observed RRBs in ASD.

Central autonomic network dysfunction and plasma Alzheimer’s disease biomarkers in older adults

BackgroundHigher order regulation of autonomic function is maintained by the coordinated activity of specific cortical and subcortical brain regions, collectively referred to as the central autonomic network (CAN). Autonomic changes are frequently observed in Alzheimer’s disease (AD) and dementia, but no studies to date have investigated whether plasma AD biomarkers are associated with CAN functional connectivity changes in at risk older adults.MethodsIndependently living older adults (N = 122) without major neurological or psychiatric disorder were recruited from the community. Participants underwent resting-state brain fMRI and a CAN network derived from a voxel-based meta-analysis was applied for overall, sympathetic, and parasympathetic CAN connectivity using the CONN Functional Toolbox. Sensorimotor network connectivity was studied as a negative control. Plasma levels of amyloid (Aβ42, Aβ40), neurofilament light chain (NfL), and glial fibrillary acidic protein (GFAP) were assessed using digital immunoassay. The relationship between plasma AD biomarkers and within-network functional connectivity was studied using multiple linear regression adjusted for demographic covariates and Apolipoprotein E (APOE) genotype. Interactive effects with APOE4 carrier status were also assessed.ResultsAll autonomic networks were positively associated with Aβ42/40 ratio and remained so after adjustment for age, sex, and APOE4 carrier status. Overall and parasympathetic networks were negatively associated with GFAP. The relationship between the parasympathetic CAN and GFAP was moderated by APOE4 carrier status, wherein APOE4 carriers with low parasympathetic CAN connectivity displayed the highest plasma GFAP concentrations (B = 910.00, P = .004). Sensorimotor connectivity was not associated with any plasma AD biomarkers, as expected.ConclusionThe present study findings suggest that CAN function is associated with plasma AD biomarker levels. Specifically, lower CAN functional connectivity is associated with decreased plasma Aβ42/40, indicative of cerebral amyloidosis, and increased plasma GFAP in APOE4 carriers at risk for AD. These findings could suggest higher order autonomic and parasympathetic dysfunction in very early-stage AD, which may have clinical implications.

Long-Term Disease Course of Pontocerebellar Hypoplasia Type 10

BackgroundPontocerebellar hypoplasia type 10 (PCH10) due to CLP1 gene mutations is characterized by structural brain anomalies, progressive microcephaly, severe intellectual and physical disabilities, and spasticity. In this follow-up study, evolution of phenotypic and neurological characteristics of patients with PCH10 is discussed. MethodsPhenotype, growth parameters, motor functions, developmental tests, spasticity assessments, functional independence assessments, electroencephalography (EEG), and brain magnetic resonance imaging (MRI) of 10 patients with PCH10 were monitored on separate examinations. Alterations were recorded. ResultsPatients were followed-up for an average of 2.83 years. The tone of the upper extremities was significantly higher than that of the lower extremities, according to Modified Ashworth Scale (MAS) values. Sixty percent of patients could sit unsupported; 20% achieved supported sitting initially but lost the ability during follow-up. Absence of grabbing or sitting was observed in 20% of patients. During follow-up, one person achieved supported sitting and one person achieved head holding. Only one patient was able to speak a few words. Cerebellar atrophy (two of 10), pons hypoplasia (four of 10), cortical atrophy (seven of 10), enlarged ventricles (10 of 10), thinning of the corpus callosum (10 of 10), hypomyelination (six of 10), and increased white matter signal intensity (six of 10) were the observed MRI findings. ConclusionsProgressive cerebral and cerebellar atrophy was demonstrated radiologically for the first time in a PCH10 cohort. It is of crucial importance to identify these patients promptly with the help of dysmorphic findings and spasticity being pronounced in the upper extremities. Furthermore, we note that phenotypic and neurological examination findings tend to change slightly over time.

Belief inference for hierarchical hidden states in spatial navigation

Uncertainty abounds in the real world, and in environments with multiple layers of unobservable hidden states, decision-making requires resolving uncertainties based on mutual inference. Focusing on a spatial navigation problem, we develop a Tiger maze task that involved simultaneously inferring the local hidden state and the global hidden state from probabilistically uncertain observation. We adopt a Bayesian computational approach by proposing a hierarchical inference model. Applying this to human task behaviour, alongside functional magnetic resonance brain imaging, allows us to separate the neural correlates associated with reinforcement and reassessment of belief in hidden states. The imaging results also suggest that different layers of uncertainty differentially involve the basal ganglia and dorsomedial prefrontal cortex, and that the regions responsible are organised along the rostral axis of these areas according to the type of inference and the level of abstraction of the hidden state, i.e. higher-order state inference involves more anterior parts.

Non-invasive electrical brain stimulation modulates human conscious perception of mental representation

Mental representation is a key concept in cognitive science; nevertheless, its neural foundations remain elusive. We employed non-invasive electrical brain stimulation and functional magnetic resonance imaging to address this. During this process, participants perceived flickering red and green visual stimuli, discerning them either as distinct, non-fused colours or as a mentally generated, fused colour (orange). The application of transcranial alternating current stimulation to the medial prefrontal region (a key node of the default-mode network) suppressed haemodynamic activation in higher-order subthalamic and central executive networks associated with the perception of fused colours. This implies that higher-order thalamocortical and default-mode networks are crucial in humans’ conscious perception of mental representation.