Artifact removal in resting state fMRI (rfMRI) data remains a serious challenge, with even subtle head motion undermining reliability and reproducibility. Here we compared some of the most popular single-echo de-noising methods—regression of Motion parameters, White matter and Cerebrospinal fluid signals (MWC method), FMRIB’s ICA-based X-noiseifier (FIX) and ICA-based Automatic Removal Of Motion Artifacts (ICA-AROMA)—with a multi-echo approach (ME-ICA) that exploits the linear dependency of BOLD on the echo time. Data were acquired using a clinical scanner and included 30 young, healthy participants (minimal head motion) and 30 Attention Deficit Hyperactivity Disorder patients (greater head motion). De-noising effectiveness was assessed in terms of data quality after each cleanup procedure, ability to uncouple BOLD signal and motion and preservation of default mode network (DMN) functional connectivity. Most cleaning methods showed a positive impact on data quality. However, based on the investigated metrics, ME-ICA was the most robust. It minimized the impact of motion on FC even for high motion participants and preserved DMN functional connectivity structure. The high-quality results obtained using ME-ICA suggest that using a multi-echo EPI sequence, reliable rfMRI data can be obtained in a clinical setting.

Our findings show a short-term beneficial effect of motor rehabilitation on gait performances in MS, accompanied by brain functional reorganization in the sensory-motor network.

One of the main limitations of pharmacological fMRI is its inability to provide a molecular insight into the main effect of compounds, leaving an open question about the relationship between drug effects and haemodynamic response. The aim of this study is to investigate the acute effects of 3,4-methylenedioxymethamphetamine (MDMA) on functional connectivity (FC) using a novel multimodal method (Receptor-Enriched Analysis of functional Connectivity by Targets - REACT). This approach enriches the resting state (rs-)fMRI analysis with the molecular information about the distribution density of serotonin receptors in the brain, given the serotonergic action of MDMA. Twenty healthy subjects participated in this double-blind, placebo-controlled, crossover study. A high-resolution in vivo atlas of four serotonin receptors (5-HT 1A , 5-HT 1B , 5-HT 2A , and 5-HT 4 ) and its transporter (5-HTT) was used as a template in a two-step multivariate regression analysis to estimate the spatial maps reflecting the whole-brain connectivity behaviour related to each target under placebo and MDMA. Results showed that the networks exhibiting significant changes after MDMA administration are the ones informed by the 5-HTT and 5-HT 1A distribution density maps, which are the main targets of this compound. Changes in the 5-HT 1A -enriched functional maps were also associated with the pharmacokinetic levels of MDMA and MDMA-induced FC changes in the 5-HT 2A -enriched maps correlated with the spiritual experience subscale of the Altered States of Consciousness Questionnaire. By enriching the rs-fMRI analysis with molecular data of voxel-wise distribution of the serotonin receptors across the brain, we showed that MDMA effects on FC can be understood through the distribution of its main targets. This result supports the ability of this method to characterise the specificity of the functional response of the brain to MDMA binding to serotonergic receptors, paving the way to the definition of a new fingerprint in the characterization of new compounds and potentially to a further understanding to the response to treatment.

The analysis of structural and functional neuroimaging data using graph theory has increasingly become a popular approach for visualising and understanding anatomical and functional relationships between different cerebral areas. In this work we applied a network-based approach for brain PET studies using population-based covariance matrices, with the aim to explore topological tracer kinetic differences in cross-sectional investigations. Simulations, test-retest studies and applications to cross-sectional datasets from three different tracers ([ 18 F]FDG, [ 18 F]FDOPA and [ 11 C]SB217045) and more than 400 PET scans were investigated to assess the applicability of the methodology in healthy controls and patients. A validation of statistics, including the assessment of false positive differences in parametric versus permutation testing, was also performed. Results showed good reproducibility and general applicability of the method within the range of experimental settings typical of PET neuroimaging studies, with permutation being the method of choice for the statistical analysis. The use of graph theory for the quantification of [ 18 F]FDG brain PET covariance, including the definition of an entropy metric, proved to be particularly relevant for Alzheimer’s disease, showing an association with the progression of the pathology. This study shows that covariance statistics can be applied to PET neuroimaging data to investigate the topological characteristics of the tracer kinetics and its related targets, although sensitivity to experimental variables, group inhomogeneities and image resolution need to be considered when the method is applied to cross-sectional studies.

We assessed here functional connectivity changes in the locus coeruleus (LC) and ventral tegmental area (VTA) of patients with Alzheimer's disease (AD). We recruited 169 patients with either AD or amnestic mild cognitive impairment due to AD and 37 elderly controls who underwent cognitive and neuropsychiatric assessments and resting-state functional magnetic resonance imaging at 3T. Connectivity was assessed between LC and VTA and the rest of the brain. In amnestic mild cognitive impairment patients, VTA disconnection was predominant with parietal regions, while in AD patients, it involved the posterior nodes of the default-mode network. We also looked at the association between neuropsychiatric symptoms (assessed by the neuropsychiatric inventory) and VTA connectivity. Symptoms such as agitation, irritability, and disinhibition were associated with VTA connectivity with the parahippocampal gyrus and cerebellar vermis, while sleep and eating disorders were associated with VTA connectivity to the striatum and the insular cortex. This suggests a contribution of VTA degeneration to AD pathophysiology and to the occurrence of neuropsychiatric symptoms. We did not find evidence of LC disconnection, but this could be explained by the size of this nucleus, which makes it difficult to isolate. These results are consistent with animal findings and have potential implications for AD prognosis and therapies.

Artifact removal from resting state fMRI data is an essential step for a better identification of the resting state networks and the evaluation of their functional connectivity (FC), especially in pathological conditions. There is growing interest in the development of cleaning procedures, especially those not requiring external recordings (data-driven), which are able to remove multiple sources of artifacts. It is important that only inter-subject variability due to the artifacts is removed, preserving the between-subject variability of interest—crucial in clinical applications using clinical scanners to discriminate different pathologies and monitor their staging. In Alzheimer's disease (AD) patients, decreased FC is usually observed in the posterior cingulate cortex within the default mode network (DMN), and this is becoming a possible biomarker for AD. The aim of this study was to compare four different data-driven cleaning procedures (regression of motion parameters; regression of motion parameters, mean white matter and cerebrospinal fluid signal; FMRIB's ICA-based Xnoiseifier—FIX—cleanup with soft and aggressive options) on data acquired at 1.5 T. The approaches were compared using data from 20 elderly healthy subjects and 21 AD patients in a mild stage, in terms of their impact on within-group consistency in FC and ability to detect the typical FC alteration of the DMN in AD patients. Despite an increased within-group consistency across subjects after applying any of the cleaning approaches, only after cleaning with FIX the expected DMN FC alteration in AD was detectable. Our study validates the efficacy of artifact removal even in a relatively small clinical population, and supports the importance of cleaning fMRI data for sensitive detection of FC alterations in a clinical environment.

The study of complex systems deals with emergent behavior that arises as a result of nonlinear spatiotemporal interactions between a large number of components both within the system, as well as between the system and its environment. There is a strong case to be made that neural systems as well as their emergent behavior and disorders can be studied within the framework of complexity science. In particular, the field of neuroimaging has begun to apply both theoretical and experimental procedures originating in complexity science—usually in parallel with traditional methodologies. Here, we illustrate the basic properties that characterize complex systems and evaluate how they relate to what we have learned about brain structure and function from neuroimaging experiments. We then argue in favor of adopting a complex systems-based methodology in the study of neuroimaging, alongside appropriate experimental paradigms, and with minimal influences from noncomplex system approaches. Our exposition includes a review of the fundamental mathematical concepts, combined with practical examples and a compilation of results from the literature.

High-dimensional independent component analysis (ICA), compared to low-dimensional ICA, allows to conduct a detailed parcellation of the resting-state networks. The purpose of this study was to give further insight into functional connectivity (FC) in Alzheimer’s disease (AD) using high-dimensional ICA. For this reason, we performed both low- and high-dimensional ICA analyses of resting-state fMRI data of 20 healthy controls and 21 patients with AD, focusing on the primarily altered default-mode network (DMN) and exploring the sensory-motor network. As expected, results obtained at low dimensionality were in line with previous literature. Moreover, high-dimensional results allowed us to observe either the presence of within-network disconnections and FC damage confined to some of the resting-state subnetworks. Due to the higher sensitivity of the high-dimensional ICA analysis, our results suggest that high-dimensional decomposition in subnetworks is very promising to better localize FC alterations in AD and that FC damage is not confined to the DMN.