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Previous studies have described the structure and function of the insular cortex in terms of spatially continuous gradients. Here we assess how spatial features of insular resting state functional organization correspond to individual pain sensitivity. From a previous multicenter study, we included 107 healthy participants, who underwent resting state functional MRI scans, T1-weighted scans and quantitative sensory testing on the left forearm. Thermal and mechanical pain thresholds were determined. Connectopic mapping, a technique using non-linear representations of functional organization was employed to describe functional connectivity gradients in both insulae. Partial coefficients of determination were calculated between trend surface model parameters summarizing spatial features of gradients, modal and modality-independent pain sensitivity. The dominant connectopy captured the previously reported posteroanterior shift in connectivity profiles. Spatial features of dominant connectopies in the right insula explained significant amounts of variance in thermal (R2 = 0.076; p 2 = 0.031; p 2 = 0.072; p

Background The lack of nonparametric statistical tests for confounding bias significantly hampers the development of robust, valid, and generalizable predictive models in many fields of research. Here I propose the partial confounder test, which, for a given confounder variable, probes the null hypotheses of the model being unconfounded. Results The test provides a strict control for type I errors and high statistical power, even for nonnormally and nonlinearly dependent predictions, often seen in machine learning. Applying the proposed test on models trained on large-scale functional brain connectivity data (N= 1,865) (i) reveals previously unreported confounders and (ii) shows that state-of-the-art confound mitigation approaches may fail preventing confounder bias in several cases. Conclusions The proposed test (implemented in the package mlconfound; https://mlconfound.readthedocs.io) can aid the assessment and improvement of the generalizability and validity of predictive models and, thereby, fosters the development of clinically useful machine learning biomarkers.

Pain thresholds vary considerably across individuals and are influenced by a number of behavioral, genetic and neurobiological factors. However, the neurobiological underpinnings that account for individual differences remain to be fully elucidated. In this study, we used voxel-based morphometry (VBM) and graph theory, specifically the local clustering coefficient (CC) based on resting-state connectivity, to identify brain regions, where regional gray matter volume and network properties predicted individual pain thresholds. As a main finding, we identified a cluster in the left posterior insular cortex (IC) reaching into the left parietal operculum, including the secondary somatosensory cortex, where both regional gray matter volume and the local CC correlated with individual pain thresholds. We also performed a resting-state functional connectivity analysis using the left posterior IC as seed region, demonstrating that connectivity to the pre- as well as postcentral gyrus bilaterally; that is, to the motor and primary sensory cortices were correlated with individual pain thresholds. To our knowledge, this is the first study that applied VBM in combination with voxel-based graph theory in the context of pain thresholds. The co-location of the VBM and the local CC cluster provide first evidence that both structure and function map to the same brain region while being correlated with the same behavioral measure; that is, pain thresholds. The study highlights the importance of the posterior IC, not only for pain perception in general, but also for the determination of individual pain thresholds.

Systemic administration of cholinergic antagonist scopolamine (SCO) is a widely used model to induce experimental cognitive impairment during preclinical drug testing of putative procognitive compounds. This model, however, has limited predictive validity partly due to its peripheral side-effects, therefore objective neuroimaging measures would greatly enhance its translational value. To this end, we administered SCO and peripherally acting butylscopolamine (BSCO) in preclinical functional MRI (fMRI, 9.4T) studies and measured their effects on whisker stimulation induced fMRI activation in Wistar rats. Beside the commonly used gradient echo echo-planar imaging (GE EPI) an arterial spin labeling method was also used to elucidate the vasculature-related properties of the BOLD-response. To account for anesthesia-effects, in two separate experiments we used isoflurane and combined (isoflurane + i.p. dexmedetomidine) anesthesia. In the second experiment with GE EPI and spin echo (SE) EPI sequences the effect of donepezil (DON) was also examined. In isoflurane anesthesia with GE EPI, SCO decreased the evoked BOLD response in the barrel cortex (BC), while BSCO increased it in the anterior cingulate cortex. In the combined anesthesia SCO decreased the activation in the BC as well as in the inferior colliculus (IC) while BSCO reduced the response merely in the IC. Our results revealed that SCO attenuated the evoked BOLD activation in the BC as a probable central effect in both experiments while its other detected actions depended on the type of anesthesia or dose and the given fMRI sequences.

Associative learning and memory mechanisms drive interoceptive signaling along the gut-brain axis, thus shaping affective-emotional reactions and behavior. Specifically, learning to predict potentially harmful, visceral pain is assumed to succeed within very few trials. However, the temporal dynamics of cerebellar and cerebral fMRI signal changes underlying early acquisition and extinction of learned fear signals and the concomitant evolvement of safety learning remain incompletely understood. 3 T fMRI data of healthy individuals from three studies were uniformly processed across the whole brain and the cerebellum. All studies employed differential delay conditioning (N = 94) with one visual cue (CS⁺) being repeatedly paired with visceral pain as unconditioned stimulus (US) while a second cue remained unpaired (CS⁻). During subsequent extinction (N = 51), all CS were presented without US. Behavioral results revealed increased CS⁺-aversiveness and CS⁻-pleasantness after conditioning and diminished valence ratings for both CS following extinction. During early acquisition, the CS⁻ induced linearly increasing neural activation in the insula, midcingulate cortex, hippocampus, precuneus as well as cerebral and cerebellar somatomotor regions. The comparison between acquisition and extinction phases yielded a CS⁻-induced linear increase in the posterior cingulate cortex and precuneus during early acquisition, while there was no evidence for linear fMRI signal changes for the CS⁺ during acquisition and for both CS during extinction. Based on theoretical accounts of discrimination and temporal difference learning, these results suggest a gradual evolvement of learned safety cues that engage emotional arousal, memory, and cortical modulatory networks. As safety signals are presumably more difficult to learn and to discriminate from learned threat cues, the underlying temporal dynamics may reflect enhanced salience and prediction processing as well as increasing demands for attentional resources and the integration of multisensory information. Maladaptive responses to learned safety signals are a clinically relevant phenotype in multiple conditions, including chronic visceral pain, and can be exceptionally resistant to modification or extinction. Through sustained hypervigilance, safety seeking constitutes one key component in pain and stress-related avoidance behavior, calling for future studies targeting the mechanisms of safety learning and extinction to advance current cognitive-behavioral treatment approaches.

The application of brain atlas technique is not evident in the post-processing of MR images in patients with intracranial spaceoccupying lesions since brain structures are shifted...

Background and purpose Diffusion tensor imaging (DTI) is a promising approach to detect the underlying brain pathology. These alterations can be seen in several diseases such as multiple sclerosis. Tract-based spatial statistics (TBSS) is an easy to use and robust way for analyzing diffusion data. The effect of acquisition parameters of DTI on TBSS has not been evaluated, especially the number of diffusion encoding directions (NDED), which is directly proportional with scan time. Methods We analyzed a large set of DTI data of healthy controls (N = 126) and multiple sclerosis patients (N = 78). The highest NDED (60 directions) was reduced and a tensor calculation was done separately for every subset. We calculated the mean and standard deviation of DTI parameters under the white matter mask. Moreover, the FMRIB Software Library TBSS pipeline was used on DTI images with 15, 30, 45, and 60 directions to compare differences between groups. Mean DTI parameters were compared between groups as a function of NDED. Results The mean value of FA and AD decreased with increasing number of directions. This was more pronounced in areas with smaller FA values. RD and MD were constant. The skeleton size reduced with elevating NDED along with the number of significant voxels. The TBSS analysis showed significant differences between groups throughout the majority of the skeleton and the group difference was associated with NDED. Conclusion Our results suggested that results of TBSS depended on the NDED, which should be considered when comparing DTI data with varying protocols.

Individual differences in pain perception are of interest in basic and clinical research as altered pain sensitivity is both a characteristic and a risk factor for many pain conditions. It is, however, unclear how individual sensitivity to pain is reflected in the pain-free resting-state brain activity and functional connectivity. Here, we identify and validate a network pattern in the pain-free resting-state functional brain connectome that is predictive of interindividual differences in pain sensitivity. Our predictive network signature allows assessing the individual sensitivity to pain without applying any painful stimulation, as might be valuable in patients where reliable behavioural pain reports cannot be obtained. Additionally, as a direct, non-invasive readout of the supraspinal neural contribution to pain sensitivity, it may have implications for translational research and the development and assessment of analgesic treatment strategies., An fMRI-based brain signature to predict an individual’s pain sensitivity could be useful clinically. Here the authors identify a network in the resting brain which can be used to predict responses to noxious stimuli in healthy subjects.

In a recent study, Dadi and colleagues make recommendations on optimal parameters for functional connectome-based predictive models. While the authors acknowledge that “optimal choices of parameters will differ on datasets with very different properties”, some questions regarding the universality of the recommended “default values” remain unanswered.Namely, as already briefly discussed by Dadi et al., the datasets used in the target study might not be representative regarding the sparsity of the (hidden) ground truth (i.e. the number of non-informative connections), which might affect the performance of L1- and L2-regularization approaches and feature selection.Here we exemplify that, at least in one of the investigated datasets systematic motion artefacts might bias the discriminative signal towards “non-sparsity”, which might lead to underestimating the performance of L1-regularized models and feature selection.We conclude that the expected sparsity of the discriminative signal should be carefully considered when planning predictive modelling workflows and the neuroscientific validity of predictive models should be investigated to account for non-neural confounds.

While cerebellar alterations may play a crucial role in the development of core autism spectrum disorder (ASD) symptoms, their pathophysiology on the function of cerebrocerebellar circuit loops is largely unknown. We combined multimodal MRI (9.4 T) brain assessment of the prenatal rat valproate (VPA) model and correlated immunohistological analysis of the cerebellar Purkinje cell number to address this question. We hypothesized that a suitable functional MRI (fMRI) paradigm might show some altered activity related to disrupted cerebrocerebellar information processing. Two doses of maternal VPA (400 and 600 mg/kg, s.c.) were used. The higher VPA dose induced 3% smaller whole brain volume, the lower dose induced 2% smaller whole brain volume and additionally a focal gray matter density decrease in the cerebellum and brainstem. Increased cortical BOLD responses to whisker stimulation were detected in both VPA groups, but it was more pronounced and extended to cerebellar regions in the 400 mg/kg VPA group. Immunohistological analysis revealed a decreased number of Purkinje cells in both VPA groups. In a detailed analysis, we revealed that the Purkinje cell number interacts with the cerebral BOLD response distinctively in the two VPA groups that highlights atypical function of the cerebrocerebellar circuit loops with potential translational value as an ASD biomarker.

BACKGROUND The aim of this study was to reveal potential sources of systematic motion artifacts in stroke functional magnetic resonance imaging (fMRI) focusing on those causing stimulus-correlated motion on the individual-level and separate the motion effect on the fMRI signal changing from the activation-induced alteration at population level. METHODS Eleven ischemic stroke patients were examined by fMRI. The fMRI paradigm was based on passive ankle movement on both the healthy and the paretic leg's side. Three individual-level motion correction strategies were compared and we introduced five measures to characterize each subjects' in-scanner relative head movement. After analyzing the correlation of motion parameters and the subjects’ physiological scale scores, we selected a parameter to model the motion-related artifacts in the second-level analysis. RESULTS At first (individual) level analysis, the noise-component correction-based CompCor method provided the highest −log10(p) value of cluster-level occurrence probability at 12.4/13.6 for healthy and paretic side stimulus, respectively, with a maximal z-value of 15/16.3. Including the motion parameter at second (group) level resulted in lower cluster occurrence values at 10.9/5.55 while retaining the maximal z-value. CONCLUSIONS We proposed a postprocessing pipeline for ischemic stroke fMRI data that combine the CompCor correction at first level with the modeling of motion effect at second-level analysis by a parameter obtained from fMRI data. Our solution is applicable for any fMRI-based stroke rehabilitation study since it does not require any MRI-compatible motion capture system and is based on commonly used methods.

PurposeTo explore intrahemispheric, cortico-cortical EEG functional connectivity (EEGfC) in benign childhood epilepsy with rolandic spikes (BECTS).Methods21-channel EEG was recorded in 17 non-medicated BECTS children and 19 healthy controls. 180s of spike- and artifact-free activity was selected for EEGfC analysis. Correlation of Low Resolution Electromagnetic Tomography- (LORETA-) defined current source density time series were computed between two cortical areas (region of interest, ROI). Analyses were based on broad-band EEGfC results. Groups were compared by statistical parametric network (SPN) method. Statistically significant differences between group EEGfC values were emphasized at p

The threshold-free cluster enhancement (TFCE) approach integrates cluster information into voxel-wise statistical inference to enhance detectability of neuroimaging signal. Despite the significantly increased sensitivity, the application of TFCE is limited by several factors: (i) generalisation to data structures, like brain network connectivity data is not trivial, (ii) TFCE values are in an arbitrary unit, therefore, P-values can only be obtained by a computationally demanding permutation-test. Here, we introduce a probabilistic approach for TFCE (pTFCE), that gives a simple general framework for topology-based belief boosting. The core of pTFCE is a conditional probability, calculated based on Bayes' rule, from the probability of voxel intensity and the threshold-wise likelihood function of the measured cluster size. In this paper, we provide an estimation of these distributions based on Gaussian Random Field theory. The conditional probabilities are then aggregated across cluster-forming thresholds by a novel incremental aggregation method. pTFCE is validated on simulated and real fMRI data. The results suggest that pTFCE is more robust to various ground truth shapes and provides a stricter control over cluster "leaking" than TFCE and, in many realistic cases, further improves its sensitivity. Correction for multiple comparisons can be trivially performed on the enhanced P-values, without the need for permutation testing, thus pTFCE is well-suitable for the improvement of statistical inference in any neuroimaging workflow. Implementation of pTFCE is available at https://spisakt.github.io/pTFCE.

Bilateral common carotid artery occlusion (BCCAo) in the rat is a widely used animal model of vascular dementia and a valuable tool for preclinical pharmacological drug testing, although the varying degrees of acute focal ischemic lesions it induces could interfere with its translational value. Recently, a modification to the BCCAo model, the stepwise occlusion of the two carotid arteries, has been introduced. To acquire objective translatable measures, we used longitudinal multimodal magnetic resonance imaging (MRI) to assess the effects of semi-chronic (8 days) donepezil treatment in this model, with half of the Wistar rats receiving the treatment one week after the stepwise BCCAo. With an ultrahigh field MRI, we measured high-resolution anatomy, diffusion tensor imaging, cerebral blood flow measurements and functional MRI in response to whisker stimulation, to evaluate both the structural and functional effects of the donepezil treatment and stepwise BCCAo up to 5 weeks post-occlusion. While no large ischemic lesions were detected, atrophy in the striatum and in the neocortex, along with widespread white matter microstructural changes, were found. Donepezil ameliorated the transient drop in the somatosensory BOLD response in distant cortical areas, as detected 2 weeks after the occlusion but the drug had no effect on the long term structural changes. Our results demonstrate a measurable functional MRI effect of the donepezil treatment and the importance of diffusion MRI and voxel based morphometry (VBM) analysis in the translational evaluation of the rat BCCAo model.

Objective: Cortical pathology, periventricular demyelination, and lesion formation in multiple sclerosis (MS) are related (Hypothesis 1). Factors in the cerebrospinal fluid close to these compartments could possibly drive the parallel processes. Alternatively, the cortical atrophy could be caused by remote axonal transection (Hypothesis 2). Since MRI can differentiate between demyelination and axon loss, we used this imaging modality to investigate the correlation between the pattern of diffusion parameter changes in the periventricular- and deep white matter and the gray matter atrophy. Methods: High-resolution T1-weighted, FLAIR, and diffusion MRI images were acquired in 52 RRMS patients and 50 healthy, age-matched controls. We used EDSS to estimate the clinical disability. We used Tract Based Spatial Statistics to compare diffusion parameters (fractional anisotropy, mean, axial, and radial diffusivity) between groups. We evaluated global brain, white, and gray matter atrophy with SIENAX. Averaged, standard diffusion parameters were calculated in four compartment: periventricular lesioned and normal appearing white matter, non-periventricular lesioned and normal appearing white matter. PLS regression was used to identify which diffusion parameter and in which compartment best predicts the brain atrophy and clinical disability. Results: In our diffusion tensor imaging study compared to controls we found extensive alterations of fractional anisotropy, mean and radial diffusivity and smaller changes of axial diffusivity (maximal p > 0.0002) in patients that suggested demyelination in the lesioned and in the normal appearing white matter. We found significant reduction in total brain, total white, and gray matter (patients: 718.764 ± 14.968, 323.237 ± 7.246, 395.527 ± 8.050 cm3, controls: 791.772 ± 22.692, 355.350 ± 10.929, 436.422 ± 12.011 cm3; mean ± SE), (p < 0.015; p < 0.0001; p < 0.009; respectively) of patients compared to controls. The PLS analysis revealed a combination of demyelination-like diffusion parameters (higher mean and radial diffusivity in patients) in the lesions and in the non-lesioned periventricular white matter, which best predicted the gray matter atrophy (p < 0.001). Similarly, EDSS was best predicted by the radial diffusivity of the lesions and the non-lesioned periventricular white matter, but axial diffusivity of the periventricular lesions also contributed significantly (p < 0.0001). Interpretation: Our investigation showed that gray matter atrophy and white matter demyelination are related in MS but white matter axonal loss does not significantly contribute to the gray matter pathology.

Central sensitization is a key mechanism in the pathology of several neuropathic pain disorders. We aimed to investigate the underlying brain connectivity changes in a rat model of chronic pain. Non-noxious whisker stimulation was used to evoke blood-oxygen-level-dependent (BOLD) responses in a block-design functional Magnetic Resonance Imaging (fMRI) experiment on 9.4T. Measurements were repeated two days and one week after injecting complete Freund's adjuvant into the rats' whisker pad. We found that acute pain reduced activation in the barrel cortex, most probably due to a plateau effect. After one week, increased activation of the anterior cingulate cortex was found. Analyses of effective connectivity driven by stimulus-related activation revealed that chronic pain-related central sensitization manifested as a widespread alteration in the activity of the somatosensory network. Changes were mainly mediated by the anterior cingulate cortex and the striatum and affected the somatosensory and motor cortices and the superior colliculus. Functional connectivity analysis of nested BOLD oscillations justified that the anterior cingular-somatosensory interplay is a key element of network changes. Additionally, a decreased cingulo-motor functional connectivity implies that alterations also involve the output tract of the network. Our results extend the knowledge about the role of the cingulate cortex in the chronification of pain and indicate that integration of multiple connectivity analysis could be fruitful in studying the central sensitization in the pain matrix.

Purpose Absence status (AS) epilepticus with generalized spike–wave pattern is frequently found in severely ill patients in whom several disease states co-exist. The cortical generators of the ictal EEG pattern and EEG functional connectivity (EEGfC) of this condition are unknown. The present study investigated the localization of the uppermost synchronized generators of spike–wave activity in AS. Method Seven patients with late-onset AS were investigated by EEG spectral analysis, LORETA (Low Resolution Electromagnetic Tomography) source imaging, and LSC (LORETA Source Correlation) analysis, which estimates cortico-cortical EEGfC among 23 ROIs (regions of interest) in each hemisphere. Results All the patients showed generalized ictal EEG activity. Maximum Z -scored spectral power was found in the 1–6Hz and 12–14Hz frequency bands. LORETA showed that the uppermost synchronized generators of 1–6Hz band activity were localized in frontal and temporal cortical areas that are parts of the limbic system. For the 12–14Hz band, abnormally synchronized generators were found in the antero-medial frontal cortex. Unlike the rather stereotyped spectral and LORETA findings, the individual EEGfC patterns were very dissimilar. Conclusion The findings are discussed in the context of nonconvulsive seizure types and the role of the underlying cortical areas in late-onset AS. The diversity of the EEGfC patterns remains an enigma. Localizing the cortical generators of the EEG patterns contributes to understanding the neurophysiology of the condition.

The aim of this study was to reveal potential sources of systematic motion artifacts in stroke functional magnetic resonance imaging (fMRI) focusing on those causing stimulus-correlated motion on the individual-level and separate the motion effect on the fMRI signal changing from the activation-induced alteration at population level.Eleven ischemic stroke patients were examined by fMRI. The fMRI paradigm was based on passive ankle movement on both the healthy and the paretic leg's side. Three individual-level motion correction strategies were compared and we introduced five measures to characterize each subjects' in-scanner relative head movement. After analyzing the correlation of motion parameters and the subjects' physiological scale scores, we selected a parameter to model the motion-related artifacts in the second-level analysis.At first (individual) level analysis, the noise-component correction-based CompCor method provided the highest -log10(p) value of cluster-level occurrence probability at 12.4/13.6 for healthy and paretic side stimulus, respectively, with a maximal z-value of 15/16.3. Including the motion parameter at second (group) level resulted in lower cluster occurrence values at 10.9/5.55 while retaining the maximal z-value.We proposed a postprocessing pipeline for ischemic stroke fMRI data that combine the CompCor correction at first level with the modeling of motion effect at second-level analysis by a parameter obtained from fMRI data. Our solution is applicable for any fMRI-based stroke rehabilitation study since it does not require any MRI-compatible motion capture system and is based on commonly used methods.

Summary Aims Intrahemispheric, cortico-cortical EEG functional connectivity (fC) was investigated in untreated patients with idiopathic generalized epilepsy (IGE) in this explorative study. Patients and methods Group comparison was carried out between 19, drug-naive IGE patients and 19, matched healthy persons. 90 y 2 s of 19 channels waking, interictal background EEG signal (without epileptiform potentials) were processed to the LORETA (low resolution electromagnetic tomography) software to compute current source density for 2394 voxels representing parcels of the cerebral cortex for 25 very narrow bands of 1 Hz bandwidth (VNBs) from 1 to 25 Hz. EEG fC was investigated among the already localized sources. Pearson correlation coefficients (R) were computed among the 33 regions of interest (ROI) within the left and within the right hemisphere, separately. Group differences were computed by means of t-statistics. Corrected p Main results (1) The anatomical patterns of the fC differences showed great frequency-dependency. (2) Hemispheric asymmetry was prominent within most VNBs. (3) Decreased fC in the IGE group was found across all VNBs in the 1⿿6 Hz frequency range as compared to mixed patterns comprising both increased and decreased fC at >6 Hz frequencies. (4) In the 5⿿25 Hz range, decreased fC dominated in the anterior, increased fC in the posterior parts of the cortex. (5) The results delineated an anterior and a posterior network. Discussion (1) Decreased fC in the 1⿿6 Hz band might indicate some relationship to yet hidden structure network abnormalities. (2) The anatomical patterns of fC indicate frequency-dependent, pathological coupling and decoupling processes in the interictal state. (3) The two networks might help to understand seizure liability and seizure precipitation in IGE. Significance This is the first study to explore EEG fC in the interictal condition of IGE patients. The importance of EEG frequencies in evaluating fC in IGE was demonstrated and starting points for further research were given.

Functional Magnetic Resonance Imaging (fMRI) based brain connectivity analysis maps the functional networks of the brain by estimating the degree of synchronous neuronal activity between brain regions. Recent studies have demonstrated that “resting-state” fMRI-based brain connectivity conclusions may be erroneous when motion artifacts have a differential effect on fMRI BOLD signals for between group comparisons. A potential explanation could be that in-scanner displacement, due to rotational components, is not spatially constant in the whole brain. However, this localized nature of motion artifacts is poorly understood and is rarely considered in brain connectivity studies. In this study, we initially demonstrate the local correspondence between head displacement and the changes in the resting-state fMRI BOLD signal. Than, we investigate how connectivity strength is affected by the population-level variation in the spatial pattern of regional displacement. We introduce Regional Displacement Interaction (RDI), a new covariate parameter set for second-level connectivity analysis and demonstrate its effectiveness in reducing motion related confounds in comparisons of groups with different voxel-vise displacement pattern and preprocessed using various nuisance regression methods. The effect of using RDI as second-level covariate is than demonstrated in autism-related group comparisons. The relationship between the proposed method and some of the prevailing subject-level nuisance regression techniques is evaluated. Our results show that, depending on experimental design, treating in-scanner head motion as a global confound may not be appropriate. The degree of displacement is highly variable among various brain regions, both within and between subjects. These regional differences bias correlation-based measures of brain connectivity. The inclusion of the proposed second-level covariate into the analysis successfully reduces artifactual motion-related group differences and preserves real neuronal differences, as demonstrated by the autism-related comparisons.

To investigate the effect of chronic VPA treatment of EEG functional connectivity in successfully treated idiopathic generalized epilepsy (IGE) patients.19-channel waking, resting-state EEG records of 26 IGE patients were analyzed before treatment (IGE) and after the 90th day of treatment (VPA), in seizure-free condition. Three minutes of artifact-free EEG background activity (without epileptiform potentials) was analyzed for each patient in both conditions. A group of 26 age-matched healthy normative control persons (NC) was analyzed in the same way. All the EEG samples were processed to LORETA (Low Resolution Electromagnetic Tomography) to localize multiple distributed sources of EEG activity. Current source density time series were generated for 33 regions of interest (ROI) in each hemisphere for four frequency bands. Pearson correlation coefficients (R) were computed between all ROIs in each hemisphere, for four bands across the investigated samples. R values corresponded to intrahemispheric, cortico-cortical functional EEG connectivity (EEGfC). Group and condition differences were analyzed by statistical parametric network method.p0.05, corrected for multiple comparisons: (1) The untreated IGE group showed increased EEGfC in the delta and theta bands, and decreased EEGfC in the alpha band (as compared to the NC group); (2) VPA treatment normalized EEGfC in the delta, theta and alpha bands; and (3) degree of normalization depended on frequency band and cortical region.VPA treatment normalizes EEGfC in IGE patients.

Attempts to explicate the neural abnormalities behind autism spectrum disorders frequently revealed impaired brain connectivity, yet our knowledge is limited about the alterations linked with autistic traits in the non-clinical population. In our study, we aimed at exploring the neural correlates of dimensional autistic traits using a dual approach of diffusion tensor imaging (DTI) and graph theoretical analysis of resting state functional MRI data. Subjects were sampled from a public neuroimaging dataset of healthy volunteers. Inclusion criteria were adult age (age: 18–65), availability of DTI and resting state functional acquisitions and psychological evaluation including the Social Responsiveness Scale (SRS) and Autistic Spectrum Screening Questionnaire (ASSQ). The final subject cohort consisted of 127 neurotypicals. Global brain network structure was described by graph theoretical parameters: global and average local efficiency. Regional topology was characterized by degree and efficiency. We provided measurements for diffusion anisotropy. The association between autistic traits and the neuroimaging findings was studied using a general linear model analysis, controlling for the effects of age, gender and IQ profile. Significant negative correlation was found between the degree and efficiency of the right posterior cingulate cortex and autistic traits, measured by the combination of ASSQ and SRS scores. Autistic phenotype was associated with the decrease of whole-brain local efficiency. Reduction of diffusion anisotropy was found bilaterally in the temporal fusiform and parahippocampal gyri. Numerous models describe the autistic brain connectome to be dominated by reduced long-range connections and excessive short-range fibers. Our finding of decreased efficiency supports this hypothesis although the only prominent effect was seen in the posterior limbic lobe, which is known to act as a connector hub. The neural correlates of the autistic trait in neurotypicals showed only limited similarities to the reported findings in clinical populations with low functioning autism.

Summary Introduction The neuronal mechanisms of enduring seizure propensity and seizure precipitation in juvenile myoclonic epilepsy (JME) are not known. We investigated these issues, within the framework of the “network concept” of epilepsy. Methods Design1: 19, unmedicated JME patients were compared with nineteen, age-, and sex-matched normal control persons (NC). A total of 120 s, artifact-free, paroxysm-free, eyes-closed, resting state EEG background activity was analyzed for each person. Design2: interictal and immediate preictal periods of the JME patients were compared in order to explore interictal–preictal network differences. For both comparison designs, statistically significant differences of EEG functional connectivity (EEGfC), nodal and global graph parameters were evaluated. Main results Design1: maximum abnormalities were: increased delta, theta, alpha1 EEGfC and decreased alpha2 and beta EEGfC in the JME group as compared to the NC group, mainly among cortical areas that are involved in sensory-motor integration. Nodal degree and efficiency of three, medial, basal frontal nodes were greater in JME than in NC, in the alpha1 band. Design2: preictal delta EEGfC showed further increase in the above-mentioned areas, as compared to the interictal state. Discussion Increased EEGfC indicates a hypercoupled state among the specified cortical areas. This interictal abnormality further increases in the preictal state. Nodal graph statistics indicates abnormal neuronal dynamics in the cortical area that is the ictal onset zone in JME. Significance Interictal and preictal neuronal dysfunction has been described in terms of network dynamics and topography in JME patients. Forthcoming investigations of seizure precipitation and therapeutic drug effects are encouraged on this basis.

Summary Purpose The neuronal mechanisms of remission of epilepsy are not known. Based on the principles of the “network theory of epilepsy” we postulated the existence of abnormal cortico-cortical interactions at the onset of epilepsy (Hypothesis-1), and postulated that remission is associated with the decrease or disappearance of the abnormal quantitative EEG findings (Hypothesis-2). Methods Four children with benign epilepsy with rolandic sharp waves (BERS) were investigated. 21-channel EEG was recorded at the onset of the disease (Setting No. 1) and in remission (Setting No. 2). Local EEG synchronization was estimated by LORETA (low resolution electromagnetic tomography). Remote EEG synchronization (intra-hemispheric, cortico-cortical EEG functional connectivity, EEGfC) was computed by the LSC (LORETA Source Correlation) method, among 23 regions of interest (ROI) in both hemispheres. Both local and remote EEG synchronization were evaluated in very narrow frequency bands of 1 Hz bandwidth (VNB), from 1 to 25 Hz. Results Individual results were presented. Abnormal but topographically very dissimilar LORETA and LSC findings were found at the onset of the disease. The disappearance of the initial abnormalities was found in Setting No. 2. An unforeseen finding was the presence of abnormal EEGfC results in Setting No. 2. Discussion The authors confirmed both hypotheses. The dissimilarity of the initial abnormalities is in accord with the network concept of epilepsy and the etiology of BERS. The disappearance of the initial abnormalities reflects “normalization” of network dynamics while the emergence of new EEGfC abnormalities is interpreted as “compensation”. Conclusion EEG-based local and remote connectivity (EEGfC) are appropriate tools to describe network dynamics in the active state of BERS and in remission.

The aims of this study were (I) to describe statistically the fluctuation of the goodness of automated CT-MRI registration method (2) to evaluate a numerical parameter, scaled to [0,1] interval (lambda), for characterizing the population level accuracy of any automated CT-MRI registration algorithm on voxel similarity basis. The population level distribution of crosscorrelation values between the reference T1-weighted images and the automatically registered images were investigated in five patient groups (brain metastatis, cavernoma, cranial nerve schwannoma, meningioma, trigeminal neuralgia). The evaluated distributions appeared as the mixture of two Gaussians and a peak at the 1.0 value. The evaluated distributions appeared as the mixture of two Gaussians and a peak at the 1.0 value, therefore we classified the result of automated registration into three accuracy types (AT), AT1: cross-correlation equals to 1.0, AT2: when the automatically registered image slightly differs from the reference one, cross-correlation ≈1.0, and AT3: when the crosscorrelation is about 0.4. Pauto was introduced as the ratio of well fitted automated registration relative to number of all the registrations, Cupper and Clower are the mean of AT2 and A T3 distributions. The A=Pauto *Cupper/Clower product was used as the measure of the goodness of automated image registration procedure at population level. The evaluated lambda parameter will be used to control the impacts of software modifications and to optimize the functional parameters of the evaluated preprocessing steps.