Resting-state fMRI

Visual gamma entrainment using sensory stimuli (vGENUS) is a promising non-invasive therapeutic approach for Alzheimer's disease (AD), showing efficacy in improving memory function and physiological biomarkers levels in mice and, in perspective, also human patients. However, its mechanisms of action remain poorly understood. Explanations in terms of boosted neurogenesis or glymphatic clearance have been advanced. Here we probe the alternative hypothesis that this non-invasive and non-pharmacological stimulation acts by increasing the fluidity of brain dynamics. Our hypothesis expands upon prev

…bodies (MCI-LB).We used resting state fMRI data (TR=0.98s, 580 scans) of 30 participants with MCI-LB (age 68.5 ± 6.0, 16 women) and 55 healthy controls (HC; age 67.7 ± 6.4, 33 women). The participants underwent neuropsychological examination and MRI measurement at Masaryk University in Brno (CZ). Inclusion in MCI-LB group was based on MCI-LB diagnostic criteria. Preprocessed fMRI data were analysed with Coactivation patten analysis. We anal

…functional magnetic resonance imaging (fMRI) make it possible to noninvasively measure the structure of cerebral white matter fibers and neural activity in the brain. Clinical application of these techniques is expected for early diagnosis of Parkinson's disease and evaluation of pathophysiology and treatment efficacy. In this study, we used MRI to examine changes in functional connectivity in [JAG1] mice in which 6-hydroxydopamine (6-OHDA) was unilaterally injected into the medial forebrain bundle of the mouse and which d

Patients with Alzheimer's disease (AD) often display disrupted resting-state brain functional connectivity compared to healthy individuals, as observed in resting-state fMRI studies. Understanding which brain regions contribute to this disruption is vital for understanding disease progression.We propose the Functional Connectivity Curve Detection model (FunCurvDtx) to identify key brain regions for classifying AD versus normal subjects based on functional connectivity. FunCurvDtx combines functional principal component analysis (fPCA) and group penalized logistic regression to analyze function

According to the model of the glymphatic system, the clearance of waste products from the brain is dependent on the directed flow of fluid across different compartments in the brain and associated lymphatic vessels. The coupling between the coherent global grey matter activity (gGM-activity) and cerebrospinal fluid movement (gGM-CSF-coupling) is thought to drive glymphatic fluid movement in humans. Since gGM-coherence is severely impaired in patients with schizophrenia, the glymphatic system might be impaired in the disorder. Therefore, we hypothesized aberrant glymphatic function in schizophr

…Novel fMRI tools, e.g., the correlation between global grey matter (gGM) BOLD and CSF flow signal, have enabled neuroimaging researchers to study the system’s function. Yet, no studies are looking at whe

…Here, we used resting-state fMRI to study transient brain states and their association with cognitive outcome in children and adolescents after childhood stroke. We used clustering analysis to characterize eight recurring spatial co-activation patterns (CAPs) in a cohort of 19 patients and 17 age-match

Resting-state fMRI (rsfMRI) is widely used to map brain network organization in healthy and pathological states. However, the neural underpinnings governing brainwide fMRI functional connectivity (interareal activity correlation) remain unclear. Neocortical excitatory/inhibitory (E/I) balance critically affects local and long-range information processing and can conceivably bias fMRI connectivity, as suggested by the widely reported neocortical E/I imbalance and altered fMRI connectivity in multiple brain disorders. Here, we combine chemogenetics, rsfMRI and electrophysiology to causally probe

…altered functional connectivity in resting-state networks. An unsupervised machine-learning method, sparse partial least squares analysis (SPLS) captures latent variables from clinical features and brain mechanisms, allowing the examination of relationships between functional connectivity and cognitive functions across disorders. This study aimed to explore relationships between functional connectivity, cognitive domains and study groups using SPLS. 990 participants with recent-onset p

Predicting neuromarkers for cognitive abilities using fMRI has been a major focus of research in the past few years. However, it has recently been reported that many thousands of participants are required to obtain reproducible results. This appears to be a major impediment to obtaining neuromarkers from fMRI because large sample sizes are typically not available in neuroimaging studies. Here we show that the out-of-sample prediction accuracy can be dramatically improved by supplementing fMRI with readily available non-imaging information so that reliable predictive modeling becomes feasible e

…Human Connectome Project, for which resting-state fMRI scans were available. We computed sliding window functional connectivity (sw-FC) on windows of 60 s. sw-FC matrices were appro

…Using resting-state fMRI (rs-fMRI) and MRI spectroscopy (MRS), we characterized both the functional and metabolic effects of TUS on a subcortical structure, the amygdala.In two anaesthetized non-human primates, We select

…Leveraging resting-state fMRI, a method facilitating direct cross-species comparisons, we aimed to explore Resting State Networks (RSNs) in pigeons. Our study sought to discern detectable RSNs in the awake pigeon brain and assess their spatial distribution. Our

…measuring whole-brain responses using resting-state fMRI. Specifically, we explore the impact of chemogenetically neuromodulating D1 medium spiny neurons in the dorsomedial caudate putamen (CPdm) on BOLD dynamics within a striato-thalamo-cortical circuit in mice. Our findings indicate that CPdm neuromodulation alters BOLD dynamics in thalamic subregions projecting to the dorsomedial striatu

Modest success has been made in using resting state fMRI (rs-fMRI) to predict individualized behaviors. Here, we describe a scalable and modular system for individualized representation learning of rs-fMRI. The system includes a “deep” base with “shallow” adds-on. The “deep” base is trained through multiple stages of self-supervised learning with unlabeled rs-fMRI. After the base is trained and fixed, the “shallow” adds-on are trained with supervised learning for behavior prediction. The base includes a variational auto-encoder (VAE) for unsupervised learning of spatial representations, a Bi-d

During slow-wave sleep, the brain is in a self-organized regime in which slow oscillations (SOs) between up- and down-states travel across the cortex. While an isolated piece of cortex can produce SOs, the brain-wide propagation of these oscillations are thought to be mediated by the long-range axonal connections. We address the mechanism of how SOs emerge and recruit large parts of the brain using a whole-brain model constructed from empirical connectivity data in which SOs are induced independently in each brain area by a local adaptation mechanism. Using an evolutionary optimization approac

…We analyzed resting state fMRI and genetic single nucleotide polymorphism data of 30,660 people in the UK biobank and found 15 regions in the human DNA that relate to the brain language network. We link two genomic regions on chromosome 3 (EPHA3 and TBC1D5) to left-right differences in that network. All implicated genes tend to be more expressed during prenatal development. Therefore some genetic influences likely affect language network functional connectivity in the embryo and the fetus.

Psychotic disorders are characterized by heterogeneity in etiopathology, clinical presentation and individual trajectory, complicating diagnosis and treatment. Despite the increasing amount of studies investigating significant alterations, the mechanisms underlying the emergence and progression of these disorders remains unclear. It is therefore fundamental to improve our ability to differentiate between clinical subgroups and to move towards a more personalised approach. Here, we aim to highlight critical neural correlates in early stages of psychosis, and to identify relevant biomarkers corr

The Group Iterative Multiple Model Estimation (GIMME) framework has emerged as a promising method for characterizing connections between brain regions in functional neuroimaging data. Two of the most appealing features of this framework are its ability to estimate the directionality of connections between network nodes and its ability to determine whether those connections apply to everyone in a sample (group-level) or just to one person (individual-level). However, there are outstanding questions about the validity and stability of these estimates, including: 1) how recovery of connection dir

…neuroimaging methods such as resting-state fMRI and diffusion MRI can shed light on brain network dysfunctions associated with pathology and cognitive decline from preclinical to clinical dementia. I will first present our findings from two independent datasets on how amyloid and cerebrovascular pathology influence brain functional networks cross-sectionally and longitudinally in individuals with