General anaesthesia reduces the uniqueness of brain connectivity across individuals and across species.

The human brain is characterised by idiosyncratic patterns of spontaneous thought, rendering each brain uniquely identifiable from its neural activity. However, deep general anaesthesia suppresses subjective experience. Does it also suppress what makes each brain unique? Here we used functional MRI under the effects of the general anaesthetics sevoflurane and propofol to determine whether anaesthetic-induced unconsciousness diminishes the uniqueness of the human brain: both with respect to the brains of other individuals, and the brains of another species. We report that under anaesthesia individual brains become less self-similar and less distinguishable from each other. Loss of distinctiveness is highly organised: it co-localises with the archetypal sensory-association axis, correlating with genetic and morphometric markers of phylogenetic differences between humans and other primates. This effect is more evident at greater anaesthetic depths, reproducible across sevoflurane and propofol, and reversed upon recovery. Providing convergent evidence, we show that under anaesthesia the functional connectivity of the human brain becomes more similar to the macaque brain. Finally, anaesthesia diminishes the match between spontaneous brain activity and meta-analytic brain patterns aggregated from the NeuroSynth engine. Collectively, the present results reveal that anaesthetised human brains are not only less distinguishable from each other, but also less distinguishable from the brains of other primates, with specifically human-expanded regions being the most affected by anaesthesia.

In vivo mapping of pharmacologically induced functional reorganization onto the human brain's neurotransmitter landscape.

To understand how pharmacological interventions can exert their powerful effects on brain function, we need to understand how they engage the brain's rich neurotransmitter landscape. Here, we bridge microscale molecular chemoarchitecture and pharmacologically induced macroscale functional reorganization, by relating the regional distribution of 19 neurotransmitter receptors and transporters obtained from positron emission tomography, and the regional changes in functional magnetic resonance imaging connectivity induced by 10 different mind-altering drugs: propofol, sevoflurane, ketamine, lysergic acid diethylamide (LSD), psilocybin, N,N-Dimethyltryptamine (DMT), ayahuasca, 3,4-methylenedioxymethamphetamine (MDMA), modafinil, and methylphenidate. Our results reveal a many-to-many mapping between psychoactive drugs' effects on brain function and multiple neurotransmitter systems. The effects of both anesthetics and psychedelics on brain function are organized along hierarchical gradients of brain structure and function. Last, we show that regional co-susceptibility to pharmacological interventions recapitulates co-susceptibility to disorder-induced structural alterations. Collectively, these results highlight rich statistical patterns relating molecular chemoarchitecture and drug-induced reorganization of the brain's functional architecture.

Spatial signatures of anesthesia-induced burst-suppression differ between primates and rodents.

During deep anesthesia, the electroencephalographic (EEG) signal of the brain alternates between bursts of activity and periods of relative silence (suppressions). The origin of burst-suppression and its distribution across the brain remain matters of debate. In this work, we used functional magnetic resonance imaging (fMRI) to map the brain areas involved in anesthesia-induced burst-suppression across four mammalian species: humans, long-tailed macaques, common marmosets, and rats. At first, we determined the fMRI signatures of burst-suppression in human EEG-fMRI data. Applying this method to animal fMRI datasets, we found distinct burst-suppression signatures in all species. The burst-suppression maps revealed a marked inter-species difference: in rats, the entire neocortex engaged in burst-suppression, while in primates most sensory areas were excluded-predominantly the primary visual cortex. We anticipate that the identified species-specific fMRI signatures and whole-brain maps will guide future targeted studies investigating the cellular and molecular mechanisms of burst-suppression in unconscious states.

The development of anesthesia was a significant advance in medicine. It allows individuals to undergo surgery without feeling pain or remembering the experience. But scientists still do not know how anesthesia works. During anesthesia, scientists have measured brain activity using electroencephalograms (EEG) and found that the brain appears to turn on and off. Comatose patients also have similar switches between bursts of electrical activity and periods of silence. This burst-suppression pattern may be related to unconsciousness. But scientists still have many questions about how anesthesia causes burst-suppression. One challenge is that while an EEG can tell scientists when the brain turns on and off, it does not show exactly where this occurs. Another imaging method called functional Magnetic Resonance Imaging (fMRI) may fill this gap by allowing scientists to map where the brain activity occurs. Sirmpilatze et al. have created detailed maps of burst-suppression in humans, primates, and rats under anesthesia by analyzing brain scans using fMRI. In rats, the entire outer layer or cortex of the brain underwent a synchronized pattern of burst-suppression. In humans and primates, areas of the brain like those responsible for eyesight did not follow the rest of the cortex in switching on and off. The experiments reveal crucial differences in how rats and humans and other primates respond to anesthesia. The fMRI mapping technique Sirmpilatze et al. created may help scientists learn more about these differences and why some parts of human brains do not undergo burst-suppression. This may help scientists learn more about unconsciousness and help improve anesthesia or the care of comatose patients.

Criticality Creates a Functional Platform for Network Transitions Between Internal and External Processing Modes in the Human Brain.

Continuous switching between internal and external modes in the brain appears important for generating models of the self and the world. However, how the brain transitions between these two modes remains unknown. We propose that a large synchronization fluctuation of brain networks, emerging only near criticality (i.e., a balanced state between order and disorder), spontaneously creates temporal windows with distinct preferences for integrating the network's internal information or for processing external stimuli. Using a computational model, electroencephalography (EEG) analysis, and functional magnetic resonance imaging (fMRI) analysis during alterations of consciousness in humans, we report that synchronized and incoherent networks, respectively, bias toward internal and external information with specific network configurations. In the brain network model and EEG-based network, the network preferences are the most prominent at criticality and in conscious states associated with the bandwidth 4-12 Hz, with alternating functional network configurations. However, these network configurations are selectively disrupted in different states of consciousness such as general anesthesia, psychedelic states, minimally conscious states, and unresponsive wakefulness syndrome. The network preference for internal information integration is only significant in conscious states and psychedelic states, but not in other unconscious states, suggesting the importance of internal information integration in maintaining consciousness. The fMRI co-activation pattern analysis shows that functional networks that are sensitive to external stimuli-such as default mode, dorsal attentional, and frontoparietal networks-are activated in incoherent states, while insensitive networks, such as global activation and deactivation networks, are dominated in highly synchronized states. We suggest that criticality produces a functional platform for the brain's capability for continuous switching between two modes, which is crucial for the emergence of consciousness.

Dynamic Patterns of Global Brain Communication Differentiate Conscious From Unconscious Patients After Severe Brain Injury.

The neurophysiology of the subjective sensation of being conscious is elusive; therefore, it remains controversial how consciousness can be recognized in patients who are not responsive but seemingly awake. During general anesthesia, a model for the transition between consciousness and unconsciousness, specific covariance matrices between the activity of brain regions that we call patterns of global brain communication reliably disappear when people lose consciousness. This functional magnetic imaging study investigates how patterns of global brain communication relate to consciousness and unconsciousness in a heterogeneous sample during general anesthesia and after brain injury. First, we describe specific patterns of global brain communication during wakefulness that disappear during propofol (n = 11) and sevoflurane (n = 14) general anesthesia. Second, we search for these patterns in a cohort of unresponsive wakeful patients (n = 18) and unmatched healthy controls (n = 20) in order to evaluate their potential use in clinical practice. We found that patterns of global brain communication characterized by high covariance in sensory and motor areas or low overall covariance and their dynamic change were strictly associated with intact consciousness in this cohort. In addition, we show that the occurrence of these two patterns is significantly related to activity within the frontoparietal network of the brain, a network known to play a crucial role in conscious perception. We propose that this approach potentially recognizes consciousness in the clinical routine setting.

Brain network integration dynamics are associated with loss and recovery of consciousness induced by sevoflurane.

The dynamic interplay of integration and segregation in the brain is at the core of leading theoretical accounts of consciousness. The human brain dynamically alternates between a sub-state where integration predominates, and a predominantly segregated sub-state, with different roles in supporting cognition and behaviour. Here, we combine graph theory and dynamic functional connectivity to compare resting-state functional MRI data from healthy volunteers before, during, and after loss of responsiveness induced with different concentrations of the inhalational anaesthetic, sevoflurane. We show that dynamic states characterised by high brain integration are especially vulnerable to general anaesthesia, exhibiting attenuated complexity and diminished small-world character. Crucially, these effects are reversed upon recovery, demonstrating their association with consciousness. Higher doses of sevoflurane (3% vol and burst-suppression) also compromise the temporal balance of integration and segregation in the human brain. Additionally, we demonstrate that reduced anticorrelations between the brain's default mode and executive control networks dynamically reconfigure depending on the brain's state of integration or segregation. Taken together, our results demonstrate that the integrated sub-state of brain connectivity is especially vulnerable to anaesthesia, in terms of both its complexity and information capacity, whose breakdown represents a generalisable biomarker of loss of consciousness and its recovery.

Changes in Whole Brain Dynamics and Connectivity Patterns during Sevoflurane- and Propofol-induced Unconsciousness Identified by Functional Magnetic Resonance Imaging.

BACKGROUND: A key feature of the human brain is its capability to adapt flexibly to changing external stimuli. This capability can be eliminated by general anesthesia, a state characterized by unresponsiveness, amnesia, and (most likely) unconsciousness. Previous studies demonstrated decreased connectivity within the thalamus, frontoparietal, and default mode networks during general anesthesia. We hypothesized that these alterations within specific brain networks lead to a change of communication between networks and their temporal dynamics. METHODS: We conducted a pooled spatial independent component analysis of resting-state functional magnetic resonance imaging data obtained from 16 volunteers during propofol and 14 volunteers during sevoflurane general anesthesia that have been previously published. Similar to previous studies, mean z-scores of the resulting spatial maps served as a measure of the activity within a network. Additionally, correlations of associated time courses served as a measure of the connectivity between networks. To analyze the temporal dynamics of between-network connectivity, we computed the correlation matrices during sliding windows of 1 min and applied k-means clustering to the matrices during both general anesthesia and wakefulness. RESULTS: Within-network activity was decreased in the default mode, attentional, and salience networks during general anesthesia (P < 0.001, range of median changes: -0.34, -0.13). Average between-network connectivity was reduced during general anesthesia (P < 0.001, median change: -0.031). Distinct between-network connectivity patterns for both wakefulness and general anesthesia were observed irrespective of the anesthetic agent (P < 0.001), and there were fewer transitions in between-network connectivity patterns during general anesthesia (P < 0.001, median number of transitions during wakefulness: 4 and during general anesthesia: 0). CONCLUSIONS: These results suggest that (1) higher-order brain regions play a crucial role in the generation of specific between-network connectivity patterns and their dynamics, and (2) the capability to interact with external stimuli is represented by complex between-network connectivity patterns.

Relationship of critical dynamics, functional connectivity, and states of consciousness in large-scale human brain networks.

Recent modeling and empirical studies support the hypothesis that large-scale brain networks function near a critical state. Similar functional connectivity patterns derived from resting state empirical data and brain network models at criticality provide further support. However, despite the strong implication of a relationship, there has been no principled explanation of how criticality shapes the characteristic functional connectivity in large-scale brain networks. Here, we hypothesized that the network science concept of partial phase locking is the underlying mechanism of optimal functional connectivity in the resting state. We further hypothesized that the characteristic connectivity of the critical state provides a theoretical boundary to quantify how far pharmacologically or pathologically perturbed brain connectivity deviates from its critical state, which could enable the differentiation of various states of consciousness with a theory-based metric. To test the hypothesis, we used a neuroanatomically informed brain network model with the resulting source signals projected to electroencephalogram (EEG)-like sensor signals with a forward model. Phase lag entropy (PLE), a measure of phase relation diversity, was estimated and the topography of PLE was analyzed. To measure the distance from criticality, the PLE topography at a critical state was compared with those of the EEG data from baseline consciousness, isoflurane anesthesia, ketamine anesthesia, vegetative state/unresponsive wakefulness syndrome, and minimally conscious state. We demonstrate that the partial phase locking at criticality shapes the functional connectivity and asymmetric anterior-posterior PLE topography, with low (high) PLE for high (low) degree nodes. The topographical similarity and the strength of PLE differentiates various pharmacologic and pathologic states of consciousness. Moreover, this model-based EEG network analysis provides a novel metric to quantify how far a pharmacologically or pathologically perturbed brain network is away from critical state, rather than merely determining whether it is in a critical or non-critical state.

Simultaneous EEG-PET-fMRI measurements in disorders of consciousness: an exploratory study on diagnosis and prognosis.

Previous studies could demonstrate that functional magnetic resonance imaging (fMRI), fludeoxyglucose positron emission tomography (FDG-PET), and electroencephalography (EEG) measures contain information about patients suffering from disorders of consciousness (DOC) and thus improve the clinical diagnosis. Additionally, the technical modalities were able to predict the outcome of patients. However, most studies lack proven reproducibility in a clinical setting. We here applied a standardized combined EEG/fMRI/FDG-PET measurement to a cohort of 20 patients suffering from DOC and focused on parameters that have been demonstrated to contain information about diagnosis and prognosis of these patients. We evaluated EEG band power, fMRI connectivity in networks associated with consciousness and sensory networks, as well as absolute glucose uptake in the brain as potential markers of preserved consciousness or favorable outcome. Acquired data were analyzed by a principal component analysis to identify the most important markers in a hypothesis-free manner. These were then analyzed with statistical group comparisons. Absolute FDG-PET could prove that glucose metabolism in the occipital lobe is significantly higher in minimally conscious than in vegetative state patients. Delta band power showed to be prognostic marker for a favorable outcome. We conclude that absolute FDG-PET is a suitable tool to evaluate the level consciousness in DOC patients. Additionally, we propose delta band power as marker of a favorable outcome in DOC patients. We suggest that these findings promote a standardized technical evaluation of DOC patients to improve diagnosis and prognosis.

Coherence of BOLD signal and electrical activity in the human brain during deep sevoflurane anesthesia.

INTRODUCTION: Changes in neural activity induce changes in functional magnetic resonance (fMRI) blood oxygenation level dependent (BOLD) signal. Commonly, increases in BOLD signal are ascribed to cellular excitation. OBJECTIVE: The relationship between electrical activity and BOLD signal in the human brain was probed on the basis of burst suppression EEG. This condition includes two distinct states of high and low electrical activity. METHODS: Resting-state simultaneous EEG and BOLD measurements were acquired during deep sevoflurane anesthesia with burst suppression EEG in nineteen healthy volunteers. Afterwards, fMRI volumes were assigned to one of the two states (burst or suppression) as defined by the EEG. RESULTS: In the frontal, parietal and temporal lobes as well as in the basal ganglia, BOLD signal increased after burst onset in the EEG and decreased after onset of EEG suppression. In contrast, BOLD signal in the occipital lobe was anticorrelated to electrical activity. This finding was obtained consistently in a general linear model and in raw data. CONCLUSIONS: In human brains exhibiting burst suppression EEG induced by sevoflurane, the positive correlation between BOLD signal and electrical brain activity could be confirmed in most gray matter. The exceptional behavior of the occipital lobe with an anticorrelation of BOLD signal and electrical activity might be due to specific neurovascular coupling mechanisms that are pronounced in the deeply anesthetized brain.

Neural Correlates of Sevoflurane-induced Unconsciousness Identified by Simultaneous Functional Magnetic Resonance Imaging and Electroencephalography.

BACKGROUND: The neural correlates of anesthetic-induced unconsciousness have yet to be fully elucidated. Sedative and anesthetic states induced by propofol have been studied extensively, consistently revealing a decrease of frontoparietal and thalamocortical connectivity. There is, however, less understanding of the effects of halogenated ethers on functional brain networks. METHODS: The authors recorded simultaneous resting-state functional magnetic resonance imaging and electroencephalography in 16 artificially ventilated volunteers during sevoflurane anesthesia at burst suppression and 3 and 2 vol% steady-state concentrations for 700 s each to assess functional connectivity changes compared to wakefulness. Electroencephalographic data were analyzed using symbolic transfer entropy (surrogate of information transfer) and permutation entropy (surrogate of cortical information processing). Functional magnetic resonance imaging data were analyzed by an independent component analysis and a region-of-interest-based analysis. RESULTS: Electroencephalographic analysis showed a significant reduction of anterior-to-posterior symbolic transfer entropy and global permutation entropy. At 2 vol% sevoflurane concentrations, frontal and thalamic networks identified by independent component analysis showed significantly reduced within-network connectivity. Primary sensory networks did not show a significant change. At burst suppression, all cortical networks showed significantly reduced functional connectivity. Region-of-interest-based thalamic connectivity at 2 vol% was significantly reduced to frontoparietal and posterior cingulate cortices but not to sensory areas. CONCLUSIONS: Sevoflurane decreased frontal and thalamocortical connectivity. The changes in blood oxygenation level dependent connectivity were consistent with reduced anterior-to-posterior directed connectivity and reduced cortical information processing. These data advance the understanding of sevoflurane-induced unconsciousness and contribute to a neural basis of electroencephalographic measures that hold promise for intraoperative anesthesia monitoring.

EEG entropy measures indicate decrease of cortical information processing in Disorders of Consciousness.

OBJECTIVE: Clinical assessments that rely on behavioral responses to differentiate Disorders of Consciousness are at times inapt because of some patients' motor disabilities. To objectify patients' conditions of reduced consciousness the present study evaluated the use of electroencephalography to measure residual brain activity. METHODS: We analyzed entropy values of 18 scalp EEG channels of 15 severely brain-damaged patients with clinically diagnosed Minimally-Conscious-State (MCS) or Unresponsive-Wakefulness-Syndrome (UWS) and compared the results to a sample of 24 control subjects. Permutation entropy (PeEn) and symbolic transfer entropy (STEn), reflecting information processes in the EEG, were calculated for all subjects. Participants were tested on a modified active own-name paradigm to identify correlates of active instruction following. RESULTS: PeEn showed reduced local information content in the EEG in patients, that was most pronounced in UWS. STEn analysis revealed altered directed information flow in the EEG of patients, indicating impaired feed-backward connectivity. Responses to auditory stimulation yielded differences in entropy measures, indicating reduced information processing in MCS and UWS. CONCLUSIONS: Local EEG information content and information flow are affected in Disorders of Consciousness. This suggests local cortical information capacity and feedback information transfer as neural correlates of consciousness. SIGNIFICANCE: The utilized EEG entropy analyses were able to relate to patient groups with different Disorders of Consciousness.

Adult-onset vanishing white matter disease as differential diagnosis of primary progressive multiple sclerosis: a case report.

We report the case of a 42-year-old woman with a slowly progressive cerebellar syndrome. In contrast to a relatively mild clinical presentation, the magnetic resonance imaging (MRI) showed extensive leukencephalopathy with cystic degeneration. Initially primary progressive multiple sclerosis (PPMS) was suspected. Additional diffusion-weighted imaging revealed restricted diffusion in the white matter lesions with a reduced apparent diffusion coefficient. Genetic testing showed vanishing white matter disease (VWM) with c.260C>T EIF2B3 mutation. In conclusion, in cases with relatively mild symptoms and extensive white matter lesions, adult-onset VWM should be considered as differential diagnosis of PPMS and diffusion-weighted imaging may be helpful to identify suspected cases.

Acute transverse myelitis and psoriasiform dermatitis associated with Sjoegren's syndrome: a case report.

BACKGROUND: Clinical complications of Sjoegren's syndrome include myelitis and skin manifestations. There is scarce observational data and a lack of randomised controlled studies regarding the treatment of Sjoegren's syndrome in the presence of such complications. CASE PRESENTATION: Here we report the case of a 41-year-old Caucasian female patient with biopsy-proven Sjoegren's syndrome who initially presented with generalized exanthema and subsequently developed acute extensive transverse myelitis. In view of the rapid deterioration we opted for an intensive treatment using a combination of corticosteroid pulse therapy, plasmapheresis and cyclophosphamide, which we later changed to rituximab. Under that treatment the skin manifestations resolved entirely whereas transverse myelitis showed incomplete remission. CONCLUSION: Severe neurological and dermatological complications may occur in Sjoegren's syndrome. This suggests a close yet currently unclear pathogenetic relationship. Intensive immunosuppressant treatment resulted in significant improvement of both symptom clusters. Skin manifestations may precede other severe complications in Sjoegren's syndrome and therefore require particular attention.

Fronto-parietal connectivity is a non-static phenomenon with characteristic changes during unconsciousness.

BACKGROUND: It has been previously shown that loss of consciousness is associated with a breakdown of dominating fronto-parietal feedback connectivity as assessed by electroencephalogram (EEG) recordings. Structure and strength of network connectivity may change over time. Aim of the current study is to investigate cortico-cortical connectivity at different time intervals during consciousness and unconsciousness. For this purpose, EEG symbolic transfer entropy (STEn) was calculated to indicate cortico-cortical information transfer at different transfer times. METHODS: The study was performed in 15 male volunteers. 29-channel EEG was recorded during consciousness and propofol-induced unconsciousness. EEG data were analyzed by STEn, which quantifies intensity and directionality of the mutual information flow between two EEG channels. STEn was computed over fronto-parietal channel pair combinations (10 s length, 0.5-45 Hz total bandwidth) to analyze changes of intercortical directional connectivity. Feedback (fronto → parietal) and feedforward (parieto → frontal) connectivity was calculated for transfer times from 25 ms to 250 ms in 5 ms steps. Transfer times leading to maximum directed interaction were identified to detect changes of cortical information transfer (directional connectivity) induced by unconsciousness (p<0.05). RESULTS: The current analyses show that fronto-parietal connectivity is a non-static phenomenon. Maximum detected interaction occurs at decreased transfer times during propofol-induced unconsciousness (feedback interaction: 60 ms to 40 ms, p = 0.002; feedforward interaction: 65 ms to 45 ms, p = 0.001). Strength of maximum feedback interaction decreases during unconsciousness (p = 0.026), while no effect of propofol was observed on feedforward interaction. During both consciousness and unconsciousness, intensity of fronto-parietal interaction fluctuates with increasing transfer times. CONCLUSION: Non-stationarity of directional connectivity may play a functional role for cortical network communication as it shows characteristic changes during propofol-induced unconsciousness.

Simultaneous electroencephalographic and functional magnetic resonance imaging indicate impaired cortical top-down processing in association with anesthetic-induced unconsciousness.

BACKGROUND: In imaging functional connectivity (FC) analyses of the resting brain, alterations of FC during unconsciousness have been reported. These results are in accordance with recent electroencephalographic studies observing impaired top-down processing during anesthesia. In this study, simultaneous records of functional magnetic resonance imaging (fMRI) and electroencephalogram were performed to investigate the causality of neural mechanisms during propofol-induced loss of consciousness by correlating FC in fMRI and directional connectivity (DC) in electroencephalogram. METHODS: Resting-state 63-channel electroencephalogram and blood oxygen level-dependent 3-Tesla fMRI of 15 healthy subjects were simultaneously registered during consciousness and propofol-induced loss of consciousness. To indicate DC, electroencephalographic symbolic transfer entropy was applied as a nonlinear measure of mutual interdependencies between underlying physiological processes. The relationship between FC of resting-state networks of the brain (z values) and DC was analyzed by a partial correlation. RESULTS: Independent component analyses of resting-state fMRI showed decreased FC in frontoparietal default networks during unconsciousness, whereas FC in primary sensory networks increased. DC indicated a decline in frontal-parietal (area under the receiver characteristic curve, 0.92; 95% CI, 0.68-1.00) and frontooccipital (0.82; 0.53-1.00) feedback DC (P<0.05 corrected). The changes of FC in the anterior default network correlated with the changes of DC in frontal-parietal (rpartial=+0.62; P=0.030) and frontal-occipital (+0.63; 0.048) electroencephalographic electrodes (P<0.05 corrected). CONCLUSION: The simultaneous propofol-induced suppression of frontal feedback connectivity in the electroencephalogram and of frontoparietal FC in the fMRI indicates a fundamental role of top-down processing for consciousness.

White-matter lesions drive deep gray-matter atrophy in early multiple sclerosis: support from structural MRI.

BACKGROUND: In MS, the relationship between lesions within cerebral white matter (WM) and atrophy within deep gray matter (GM) is unclear. OBJECTIVE: To investigate the spatial relationship between WM lesions and deep GM atrophy. METHODS: We performed a cross-sectional structural magnetic resonance imaging (MRI) study (3 Tesla) in 249 patients with clinically-isolated syndrome or relapsing-remitting MS (Expanded Disability Status Scale score: median, 1.0; range, 0-4) and in 49 healthy controls. Preprocessing of T1-weighted and fluid-attenuated T2-weighted images resulted in normalized GM images and WM lesion probability maps. We performed two voxel-wise analyses: 1. We localized GM atrophy and confirmed that it is most pronounced within deep GM; 2. We searched for a spatial relationship between WM lesions and deep GM atrophy; to this end we analyzed WM lesion probability maps by voxel-wise multiple regression, including four variables derived from maxima of regional deep GM atrophy (caudate and pulvinar, each left and right). RESULTS: Atrophy of each deep GM region was explained by ipsilateral WM lesion probability, in the area most densely connected to the respective deep GM region. CONCLUSION: We demonstrated that WM lesions and deep GM atrophy are spatially related. Our results are best compatible with the hypothesis that WM lesions contribute to deep GM atrophy through axonal pathology.

Mitochondrial membrane protein associated neurodegenration: a novel variant of neurodegeneration with brain iron accumulation.

BACKGROUND: Recently, mutations in an open-reading frame on chromosome 19 (C19orf12) were identified as a novel genetic factor in neurodegeneration with brain iron accumulation (NBIA). Because of the mitochondrial localization of the derived protein, this variant is referred to as mitochondrial membrane protein-associated neurodegeneration with brain iron accumulation (MPAN). METHODS/RESULTS: We describe the clinical phenotype and MRI of 3 newly identified individuals with MPAN due to either previously reported or novel homozygous or compound heterozygous genetic alterations in C19orf12. CONCLUSIONS: MPAN is characterized by a juvenile-onset, slowly progressive phenotype with predominant lower limb spasticity, generalized dystonia, and cognitive impairment. Typical additional features include axonal motor neuropathy and atrophy of the optic nerve. MRI showed iron deposition in the globus pallidus and substantia nigra without the eye-of-the-tiger sign, which is typical for PKAN, the most frequent form of NBIA.

Spatiotemporal reconfiguration of large-scale brain functional networks during propofol-induced loss of consciousness.

Applying graph theoretical analysis of spontaneous BOLD fluctuations in functional magnetic resonance imaging (fMRI), we investigated whole-brain functional connectivity of 11 healthy volunteers during wakefulness and propofol-induced loss of consciousness (PI-LOC). After extraction of regional fMRI time series from 110 cortical and subcortical regions, we applied a maximum overlap discrete wavelet transformation and investigated changes in the brain's intrinsic spatiotemporal organization. During PI-LOC, we observed a breakdown of subcortico-cortical and corticocortical connectivity. Decrease of connectivity was pronounced in thalamocortical connections, whereas no changes were found for connectivity within primary sensory cortices. Graph theoretical analyses revealed significant changes in the degree distribution and local organization metrics of brain functional networks during PI-LOC: compared with a random network, normalized clustering was significantly increased, as was small-worldness. Furthermore we observed a profound decline in long-range connections and a reduction in whole-brain spatiotemporal integration, supporting a topological reconfiguration during PI-LOC. Our findings shed light on the functional significance of intrinsic brain activity as measured by spontaneous BOLD signal fluctuations and help to understand propofol-induced loss of consciousness.

An automated tool for detection of FLAIR-hyperintense white-matter lesions in Multiple Sclerosis.

In Multiple Sclerosis (MS), detection of T2-hyperintense white matter (WM) lesions on magnetic resonance imaging (MRI) has become a crucial criterion for diagnosis and predicting prognosis in early disease. Automated lesion detection is not only desirable with regard to time and cost effectiveness but also constitutes a prerequisite to minimize user bias. Here, we developed and evaluated an algorithm for automated lesion detection requiring a three-dimensional (3D) gradient echo (GRE) T1-weighted and a FLAIR image at 3 Tesla (T). Our tool determines the three tissue classes of gray matter (GM) and WM as well as cerebrospinal fluid (CSF) from the T1-weighted image, and, then, the FLAIR intensity distribution of each tissue class in order to detect outliers, which are interpreted as lesion beliefs. Next, a conservative lesion belief is expanded toward a liberal lesion belief. To this end, neighboring voxels are analyzed and assigned to lesions under certain conditions. This is done iteratively until no further voxels are assigned to lesions. Herein, the likelihood of belonging to WM or GM is weighed against the likelihood of belonging to lesions. We evaluated our algorithm in 53 MS patients with different lesion volumes, in 10 patients with posterior fossa lesions, and 18 control subjects that were all scanned at the same 3T scanner (Achieva, Philips, Netherlands). We found good agreement with lesions determined by manual tracing (R2 values of over 0.93 independent of FLAIR slice thickness up to 6mm). These results require validation with data from other protocols based on a conventional FLAIR sequence and a 3D GRE T1-weighted sequence. Yet, we believe that our tool allows fast and reliable segmentation of FLAIR-hyperintense lesions, which might simplify the quantification of lesions in basic research and even clinical trials.