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Background: Writer's cramp is a task-specific dystonia impairing writing and sometimes other fine motor tasks. Neuroimaging studies using manifold designs have shown varying results regarding the nature of changes in the disease., Objective: To clarify and extend the knowledge of underlying changes by investigating functional connectivity (FC) in intrinsic connectivity networks with putative sensorimotor function at rest in an increased number of study subjects., Methods: Resting-state functional magnetic resonance imaging with independent component analysis was performed in 26/27 writer's cramp patients/healthy controls, and FC within and between resting state networks with putative sensorimotor function was compared. Additionally, voxel-based morphometry was carried out on the subjects' structural images., Results: Patients displayed increased left- and reduced right-hemispheric primary sensorimotor FC in the premotor-parietal network. Mostly bilaterally altered dorsal/ventral premotor FC, as well as altered parietal FC were observed within multiple sensorimotor networks and showed differing network-dependent directionality. Beyond within-network FC changes and reduced right cerebellar grey matter volume in the structural analysis, the positive between-network FC of the cerebellar network and the basal ganglia network was reduced., Conclusions: Abnormal resting-state FC in multiple networks with putative sensorimotor function may act as basis of preexisting observations made during task-related neuroimaging. Further, altered connectivity between the cerebellar and basal ganglia network underlines the important role of these structures in the disease.

Focal cortical dysplasia is a congenital abnormality of cortical development and the leading cause of surgically remediable drug-resistant epilepsy in children. Post-surgical outcome is improved by presurgical lesion detection on structural MRI. Automated computational techniques have improved detection of focal cortical dysplasias in adults but have not yet been effective when applied to developing brains. There is therefore a need to develop reliable and sensitive methods to address the particular challenges of a paediatric cohort. We developed a classifier using surface-based features to identify focal abnormalities of cortical development in a paediatric cohort. In addition to established measures, such as cortical thickness, grey-white matter blurring, FLAIR signal intensity, sulcal depth and curvature, our novel features included complementary metrics of surface morphology such as local cortical deformation as well as post-processing methods such as the "doughnut" method - which quantifies local variability in cortical morphometry/MRI signal intensity, and per-vertex interhemispheric asymmetry. A neural network classifier was trained using data from 22 patients with focal epilepsy (mean age = 12.1 ± 3.9, 9 females), after intra- and inter-subject normalisation using a population of 28 healthy controls (mean age = 14.6 ± 3.1, 11 females). Leave-one-out cross-validation was used to quantify classifier sensitivity using established features and the combination of established and novel features. Focal cortical dysplasias in our paediatric cohort were correctly identified with a higher sensitivity (73%) when novel features, based on our approach for detecting local cortical changes, were included, when compared to the sensitivity using only established features (59%). These methods may be applicable to aiding identification of subtle lesions in medication-resistant paediatric epilepsy as well as to the structural analysis of both healthy and abnormal cortical development.

Sickle cell disease (SCD) is a red blood cell disorder that causes many complications including life-long pain. Treatment of pain remains challenging due to a poor understanding of the mechanisms and limitations to characterize and quantify pain. In the present study, we examined simultaneously recording functional MRI (fMRI) and electroencephalogram (EEG) to better understand neural connectivity as a consequence of chronic pain in SCD patients. We performed independent component analysis and seed-based connectivity on fMRI data. Spontaneous power and microstate analysis was performed on EEG-fMRI data. ICA analysis showed that patients lacked activity in the default mode network (DMN) and executive control network compared to controls. EEG-fMRI data revealed that the insula cortex's role in salience increases with age in patients. EEG microstate analysis showed patients had increased activity in pain processing regions. The cerebellum in patients showed a stronger connection to the periaqueductal gray matter (involved in pain inhibition), and negative connections to pain processing areas. These results suggest that patients have reduced activity of DMN and increased activity in pain processing regions during rest. The present findings suggest resting state connectivity differences between patients and controls can be used as novel biomarkers of SCD pain.

Using quantitative T 2 * at 7 Tesla (T) magnetic resonance imaging, we investigated whether impairment in selective cognitive functions in multiple sclerosis (MS) can be explained by pathology in specific areas and/or layers of the cortex. Thirty-one MS patients underwent neuropsychological evaluation, acquisition of 7 T multi-echo T 2 * gradient-echo sequences, and 3 T anatomical images for cortical surfaces reconstruction. Seventeen age-matched healthy subjects served as controls. Cortical T 2 * maps were sampled at various depths throughout the cortex and juxtacortex. Relation between T 2 *, neuropsychological scores and a cognitive index (CI), calculated from a principal component analysis on the whole battery, was tested by a general linear model. Cognitive impairment correlated with T 2 * increase, independently from white matter lesions and cortical thickness, in cortical areas highly relevant for cognition belonging to the default-mode network (p < 0.05 corrected). Dysfunction in different cognitive functions correlated with longer T 2 * in selective cortical regions, most of which showed longer T 2 * relative to controls. For most tests, this association was strongest in deeper cortical layers. Executive dysfunction, however, was mainly related with pathology in juxtameningeal cortex. T 2 * explained up to 20% of the variance of the CI, independently of conventional imaging metrics (adjusted-R 2 : 52-67%, p < 5.10 - 4 ). Location of pathology across the cortical width and mantle showed selective correlation with impairment in differing cognitive domains. These findings may guide studies at lower field strength designed to develop surrogate markers of cognitive impairment in MS.

The formation of multiple sclerosis (MS) lesions is a complex process involving inflammation, tissue damage, and tissue repair - all of which are visible on structural magnetic resonance imaging (MRI) and potentially modifiable by pharmacological therapy. In this paper, we introduce two statistical models for relating voxel-level, longitudinal, multi-sequence structural MRI intensities within MS lesions to clinical information and therapeutic interventions: (1) a principal component analysis (PCA) and regression model and (2) function-on-scalar regression models. To do so, we first characterize the post-lesion incidence repair process on longitudinal, multi-sequence structural MRI from 34 MS patients as voxel-level intensity profiles. For the PCA regression model, we perform PCA on the intensity profiles to develop a voxel-level biomarker for identifying slow and persistent, long-term intensity changes within lesion tissue voxels. The proposed biomarker's ability to identify such effects is validated by two experienced clinicians (a neuroradiologist and a neurologist). On a scale of 1 to 4, with 4 being the highest quality, the neuroradiologist gave the score on the first PC a median quality rating of 4 (95% CI: [4,4]), and the neurologist gave the score a median rating of 3 (95% CI: [3,3]). We then relate the biomarker to the clinical information in a mixed model framework. Treatment with disease-modifying therapies (p < 0.01), steroids (p < 0.01), and being closer to the boundary of abnormal signal intensity (p < 0.01) are all associated with return of a voxel to an intensity value closer to that of normal-appearing tissue. The function-on-scalar regression model allows for assessment of the post-incidence time points at which the covariates are associated with the profiles. In the function-on-scalar regression, both age and distance to the boundary were found to have a statistically significant association with the lesion intensities at some time point. The two models presented in this article show promise for understanding the mechanisms of tissue damage in MS and for evaluating the impact of treatments for the disease in clinical trials.

Background: bvFTD patients display an impairment in the attribution of cognitive and affective states to others, reflecting GM atrophy in brain regions associated with social cognition, such as amygdala, superior temporal cortex and posterior insula. Distinctive patterns of abnormal brain functioning at rest have been reported in bvFTD, but their relationship with defective attribution of affective states has not been investigated., Objective: To investigate the relationship among resting-state brain activity, gray matter (GM) atrophy and the attribution of mental states in the behavioral variant of fronto-temporal degeneration (bvFTD)., Methods: We compared 12 bvFTD patients with 30 age- and education-matched healthy controls on a) performance in a task requiring the attribution of affective vs. cognitive mental states; b) metrics of resting-state activity in known functional networks; and c) the relationship between task-performances and resting-state metrics. In addition, we assessed a connection between abnormal resting-state metrics and GM atrophy., Results: Compared with controls, bvFTD patients showed a reduction of intra-network coherent activity in several components, as well as decreased strength of activation in networks related to attentional processing. Anomalous resting-state activity involved networks which also displayed a significant reduction of GM density. In patients, compared with controls, higher affective mentalizing performance correlated with stronger functional connectivity between medial prefrontal sectors of the default-mode and attentional/performance monitoring networks, as well as with increased coherent activity in components of the executive, sensorimotor and fronto-limbic networks., Conclusions: Some of the observed effects may reflect specific compensatory mechanisms for the atrophic changes involving regions in charge of affective mentalizing. The analysis of specific resting-state networks thus highlights an intermediate level of analysis between abnormal brain structure and impaired behavioral performance in bvFTD, reflecting both dysfunction and compensation mechanisms.

A number of studies suggest that the clinical manifestation of neurological deficits in hepatic encephalopathy results from pathologically synchronized neuronal oscillations and altered oscillatory coupling. In the present study spontaneous and evoked oscillatory brain activities were analyzed jointly with established behavioral measures of altered visual oscillatory processing. Critical flicker and fusion frequencies (CFF, FUF) were measured in 25 patients diagnosed with liver cirrhosis and 30 healthy controls. Magnetoencephalography (MEG) data were collected at rest and during a visual task employing repetitive stimulation. Resting MEG and evoked fields were analyzed. CFF and FUF were found to be reduced in patients, providing behavioral evidence for deficits in visual oscillatory processing. These alterations were found to be related to resting brain activity in patients, namely that the lower the dominant MEG frequency at rest, the lower the CFF and FUF. An analysis of evoked fields at sensor level indicated that in comparison to normal controls, patients were not able to dynamically adapt to flickering visual stimulation. Evoked activity was also analyzed based on independent components (ICs) derived by independent component analysis. The similarity between the shape of each IC and an artificial sine function representing the stimulation frequency was tested via magnitude squared coherence. In controls, we observed a small number of components that correlated strongly with the sine function and a high number of ICs that did not correlate with the sine function. Interestingly, patient data were characterized by a high number of moderately correlating components. Taken together, these results indicate a fundamental divergence of the cerebral resonance activity in cirrhotic patients.

Introduction: Cardio-metabolic risk factors have been associated with poor physical and mental health. Epidemiological studies have shown peripheral risk markers to be associated with poor cognitive functioning in normal healthy population and in disease. The aim of the study was to explore the relationship between cardio-metabolic risk factors and cortical thickness in a neurologically healthy middle aged population-based sample., Methods: T1-weighted MRI was used to create models of the cortex for calculation of regional cortical thickness in 40 adult males (average age = 50.96 years), selected from the pSoBid study. The relationship between cardio-vascular risk markers and cortical thickness across the whole brain, was examined using the general linear model. The relationship with various covariates of interest was explored., Results: Lipid fractions with greater triglyceride content (TAG, VLDL and LDL) were associated with greater cortical thickness pertaining to a number of regions in the brain. Greater C reactive protein (CRP) and intercellular adhesion molecule (ICAM-1) levels were associated with cortical thinning pertaining to perisylvian regions in the left hemisphere. Smoking status and education status were significant covariates in the model., Conclusions: This exploratory study adds to a small body of existing literature increasingly showing a relationship between cardio-metabolic risk markers and regional cortical thickness involving a number of regions in the brain in a neurologically normal middle aged sample. A focused investigation of factors determining the inter-individual variations in regional cortical thickness in the adult brain could provide further clarity in our understanding of the relationship between cardio-metabolic factors and cortical structures.

Stroke results in reorganization of residual brain networks. The functional role of brain regions within these networks remains unclear, particularly those in the contralesional hemisphere. We studied 25 stroke patients with a range of motor impairment and 23 healthy age-matched controls using magnetoencephalography (MEG) and electromyography (EMG) to measure oscillatory signals from the brain and affected muscles simultaneously during a simple isometric hand grip, from which cortico-muscular coherence (CMC) was calculated. Peaks of cortico-muscular coherence in both the beta and gamma bands were found in the contralateral sensorimotor cortex in all healthy controls, but were more widespread in stroke patients, including some peaks found in the contralesional hemisphere (7 patients for beta coherence and 5 for gamma coherence). Neither the coherence value nor the distance of the coherence peak from the mean of controls correlated with impairment. Peak CMC in the contralesional hemisphere was found not only in some highly impaired patients, but also in some patients with good functional recovery. Our results provide evidence that a wide range of cortical brain regions, including some in the contralesional hemisphere, may have influence over EMG activity in the affected muscles after stroke thereby supporting functional recovery.

Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI-related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the community's attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome.