Clinical applications of magnetic resonance imaging based functional and structural connectivity

Advances in computational neuroimaging techniques have expanded the armamentarium of imaging tools available for clinical applications in clinical neuroscience. Non-invasive, in vivo brain MRI structural and functional network mapping has been used to identify therapeutic targets, define eloquent brain regions to preserve, and gain insight into pathological processes and treatments as well as prognostic biomarkers. These tools have the real potential to inform patient-specific treatment strategies. Nevertheless, a realistic appraisal of clinical utility is needed that balances the growing excitement and interest in the field with important limitations associated with these techniques. Quality of the raw data, minutiae of the processing methodology, and the statistical models applied can all impact on the results and their interpretation. A lack of standardization in data acquisition and processing has also resulted in issues with reproducibility. This limitation has had a direct impact on the reliability of these tools and ultimately, confidence in their clinical use. Advances in MRI technology and computational power as well as automation and standardization of processing methods, including machine learning approaches, may help address some of these issues and make these tools more reliable in clinical use. In this review, we will highlight the current clinical uses of MRI connectomics in the diagnosis and treatment of neurological disorders; balancing emerging applications and technologies with limitations of connectivity analytic approaches to present an encompassing and appropriate perspective

Altered Brain Networks In Patients with Psychogenic Non-Epileptic Seizures (PNES) Using Ultra High Field MRI

Background: Psychogenic Non-epileptic Seizures (PNES) are attacks that appear similar to epileptic attacks. However, they lack abnormal electrical discharges in the brain and have psychological underpinnings and causes. The gold standard of diagnosis is video-EEG which is not widely accessible, creating a poor prognosis for patients. Resting state functional magnetic resonance imaging can aid in the diagnosis and treatment of PNES by helping better understand brain networks in patients with PNES. This study examines brain networks in patients with PNES with a focus on the default mode network and salience network. Methods: Twelve patients with PNES between the ages of 18-56 and twelve age- and sex- matched healthy participants between the ages of 18-59 were recruited. Participants underwent 7T resting-state fMRI scanning. Independent Components Analysis (ICA) and whole brain functional connectivity making use of region of interest analysis (ROI) was used to study the default mode network and the salience network. Results: No Significant differences in functional connectivity between regions in the default mode network (DMN) as well as the salience network (SN) were found when comparing patients with PNES to healthy control participants. Conclusions: In the current study patients with PNES do not show altered connectivity between brain regions in the default mode network as well as the salience network. Limitations and future directions of the current study will be discussed

Conceptual disorganization and redistribution of resting state cortical hubs in drug-naive first episode psychosis: A 7T functional magnetic resonance imaging study

Network level dysconnectivity has been studied in positive and negative symptoms of schizophrenia. Conceptual disorganization (CD) is a symptom which predicts impaired real-world functioning. Systematic reviews have reported aberrant connectivity in formal thought disorder, a construct related to CD. However, no studies have investigated whole-brain functional correlates of CD in psychosis. We sought to investigate brain regions explaining the severity of CD in patients with first-episode psychosis (FEPs) compared with healthy controls (HCs). We computed whole-brain binarized degree centrality maps of 31 FEPs, 25 HCs and characterized the patterns of network connectivity in the two groups. In FEPs, we related these findings to the severity of CD. We also studied the effect of positive and negative symptoms on altered network connectivity. Compared to HCs, the FEPs showed reduced hubness of a cluster located in the right superior temporal gyrus (rSTG). In patients exhibiting high CD, increased hubness of a medial superior parietal (mSPL) cluster was observed, compared to patients exhibiting low CD. These two regions were strongly correlated with CD scores but not with other symptom scores. Our observations are congruent with previous findings of reduced but not increased hubness. We observed increased hubness of mSPL suggesting that a cortical reorganization occurs in brain networks to provide alternate routes for information transfer. These findings provide insight into the underlying neural processes mediating the presentation of symptoms in untreated FEP. A longitudinal tracking of the symptom course will be useful to assess the mechanisms underlying these compensatory changes

Parcellation of the human sensorimotor cortex: a resting-state fMRI study

The sensorimotor cortex is a brain region comprising the primary motor cortex (MI) and the primary somatosensory (SI) cortex. In humans, investigation into these regions suggests that MI and SI are involved in the modulation and control of motor and somatosensory processing, and are somatotopically organized according to a body plan (Penfield & Boldrey, 1937). Additional investigations into somatotopic mapping in relation to the limbs in the peripheral nervous system and SI in central nervous system have further born out the importance of this body-based organization (Wall & Dubner, 1972). Understanding the nature of the sensorimotor cortex‟s structure and function has broad implications not only for human development, but also motor learning (Taubert et al., 2011) and clinical applications in structural plasticity in Parkinson‟s disease (Sehm et al., 2014), among others. The aim of the present thesis is to identify functionally meaningful subregions within the sensorimotor cortex via parcellation analysis. Previously, cerebral subregions were identified in postmortem brains by invasive procedures based on histological features (Brodmann, 1909; Vogt. & Vogt., 1919; Economo, 1926; Sanides, 1970). One widely used atlas is based on Brodmann areas (BA). Brodmann divided human brains into several areas based on the visually inspected cytoarchitecture of the cortex as seen under a microscope (Brodmann, 1909). In this atlas, BA 4, BA 3, BA 1 and BA 2 together constitute the sensorimotor cortex (Vogt. & Vogt., 1919; Geyer et al., 1999; Geyer et al., 2000). However, BAs are incapable of delineating the somatotopic detail reflected in other research (Blankenburg et al., 2003). And, although invasive approaches have proven reliable in the discovery of functional parcellation in the past, such approaches are marked by their irreversibility which, according to ethical standards, makes them unsuitable for scientific inquiry. Therefore, it is necessary to develop non-invasive approaches to parcellate functional brain regions. In the present study, a non-invasive and task-free approach to parcellate the sensorimotor cortex with resting-state fMRI was developed. This approach used functional connectivity patterns of brain areas in order to delineate functional subregions as connectivity-based parcellations (Wig et al., 2014). We selected two adjacent BAs (BA 3 and BA 4) from a standard template to cover the area along the central sulcus (Eickhoff et al., 2005). Then subregions within this area were generated using resting-state fMRI data. These subregions were organized somatotopically from medial-dorsal to ventral-lateral (corresponding roughly to the face, hand and foot regions, respectively) by comparing them with the activity maps obtained by using independent motor tasks. Interestingly, resting-state parcellation map demonstrated higher correspondence to the task-based divisions after individuals had performed motor tasks. We also observed higher functional correlations between the hand area and the foot and tongue area, respectively, than between the foot and tongue regions. The functional relevance of those subregions indicates the feasibility of a wide range of potential applications to brain mapping (Nebel et al., 2014). In sum, the present thesis provides an investigation of functional network, functional structure, and properties of the sensorimotor cortex by state-of-art neuroimaging technology. The methodology and the results of the thesis hope to carry on the future research of the sensorimotor system

The nonhuman primate neuroimaging and neuroanatomy project

Multi-modal neuroimaging projects such as the Human Connectome Project (HCP) and UK Biobank are advancing our understanding of human brain architecture, function, connectivity, and their variability across individuals using high-quality non-invasive data from many subjects. Such efforts depend upon the accuracy of non-invasive brain imaging measures. However, ‘ground truth’ validation of connectivity using invasive tracers is not feasible in humans. Studies using nonhuman primates (NHPs) enable comparisons between invasive and non-invasive measures, including exploration of how “functional connectivity” from fMRI and “tractographic connectivity” from diffusion MRI compare with long-distance connections measured using tract tracing. Our NonHuman Primate Neuroimaging & Neuroanatomy Project (NHP_NNP) is an international effort (6 laboratories in 5 countries) to: (i) acquire and analyze high-quality multi-modal brain imaging data of macaque and marmoset monkeys using protocols and methods adapted from the HCP; (ii) acquire quantitative invasive tract-tracing data for cortical and subcortical projections to cortical areas; and (iii) map the distributions of different brain cell types with immunocytochemical stains to better define brain areal boundaries. We are acquiring high-resolution structural, functional, and diffusion MRI data together with behavioral measures from over 100 individual macaques and marmosets in order to generate non-invasive measures of brain architecture such as myelin and cortical thickness maps, as well as functional and diffusion tractography-based connectomes. We are using classical and next-generation anatomical tracers to generate quantitative connectivity maps based on brain-wide counting of labeled cortical and subcortical neurons, providing ground truth measures of connectivity. Advanced statistical modeling techniques address the consistency of both kinds of data across individuals, allowing comparison of tracer-based and non-invasive MRI-based connectivity measures. We aim to develop improved cortical and subcortical areal atlases by combining histological and imaging methods. Finally, we are collecting genetic and sociality-associated behavioral data in all animals in an effort to understand how genetic variation shapes the connectome and behavior

Resting-State fMRI Advances for Functional Brain Dynamics

The development of functional magnetic resonance imaging (fMRI) in quiescent brain imaging has revealed that even at rest, brain activity is highly structured, with voxel-to-voxel comparisons consistently demonstrating a suite of resting-state networks (RSNs). Since its initial use, resting-state fMRI (RS-fMRI) has undergone a renaissance in methodological and interpretive advances that have expanded this functional connectivity understanding of brain RSNs. RS-fMRI has benefitted from the technical developments in MRI such as parallel imaging, high-strength magnetic fields, and big data handling capacity, which have enhanced data acquisition speed, spatial resolution, and whole-brain data retrieval, respectively. It has also benefitted from analytical approaches that have yielded insight into RSN causal connectivity and topological features, now being applied to normal and disease states. Increasingly, these new interpretive methods seek to advance understanding of dynamic network changes that give rise to whole brain states and behavior. This review explores the technical outgrowth of RS-fMRI from fMRI and the use of these technical advances to underwrite the current analytical evolution directed toward understanding the role of RSN dynamics in brain functioning

Brain connectivity analysis: a short survey

This short survey the reviews recent literature on brain connectivity studies. It encompasses all forms of static and dynamic connectivity whether anatomical, functional, or effective. The last decade has seen an ever increasing number of studies devoted to deduce functional or effective connectivity, mostly from functional neuroimaging experiments. Resting state conditions have become a dominant experimental paradigm, and a number of resting state networks, among them the prominent default mode network, have been identified. Graphical models represent a convenient vehicle to formalize experimental findings and to closely and quantitatively characterize the various networks identified. Underlying these abstract concepts are anatomical networks, the so-called connectome, which can be investigated by functional imaging techniques as well. Future studies have to bridge the gap between anatomical neuronal connections and related functional or effective connectivities