Robust dynamic community detection with applications to human brain functional networks.

While current technology permits inference of dynamic brain networks over long time periods at high temporal resolution, the detailed structure of dynamic network communities during human seizures remains poorly understood. We introduce a new methodology that addresses critical aspects unique to the analysis of dynamic functional networks inferred from noisy data. We propose a dynamic plex percolation method (DPPM) that is robust to edge noise, and yields well-defined spatiotemporal communities that span forward and backwards in time. We show in simulation that DPPM outperforms existing methods in accurately capturing certain stereotypical dynamic community behaviors in noisy situations. We then illustrate the ability of this method to track dynamic community organization during human seizures, using invasive brain voltage recordings at seizure onset. We conjecture that application of this method will yield new targets for surgical treatment of epilepsy, and more generally could provide new insights in other network neuroscience applications.

Deep brain stimulation of the internal capsule enhances human cognitive control and prefrontal cortex function.

Deep brain stimulation (DBS) is a circuit-oriented treatment for mental disorders. Unfortunately, even well-conducted psychiatric DBS clinical trials have yielded inconsistent symptom relief, in part because DBS' mechanism(s) of action are unclear. One clue to those mechanisms may lie in the efficacy of ventral internal capsule/ventral striatum (VCVS) DBS in both major depression (MDD) and obsessive-compulsive disorder (OCD). MDD and OCD both involve deficits in cognitive control. Cognitive control depends on prefrontal cortex (PFC) regions that project into the VCVS. Here, we show that VCVS DBS' effect is explained in part by enhancement of PFC-driven cognitive control. DBS improves human subjects' performance on a cognitive control task and increases theta (5-8Hz) oscillations in both medial and lateral PFC. The theta increase predicts subjects' clinical outcomes. Our results suggest a possible mechanistic approach to DBS therapy, based on tuning stimulation to optimize these neurophysiologic phenomena.

Simulating human sleep spindle MEG and EEG from ion channel and circuit level dynamics.

BACKGROUND: Although they form a unitary phenomenon, the relationship between extracranial M/EEG and transmembrane ion flows is understood only as a general principle rather than as a well-articulated and quantified causal chain. METHOD: We present an integrated multiscale model, consisting of a neural simulation of thalamus and cortex during stage N2 sleep and a biophysical model projecting cortical current densities to M/EEG fields. Sleep spindles were generated through the interactions of local and distant network connections and intrinsic currents within thalamocortical circuits. 32,652 cortical neurons were mapped onto the cortical surface reconstructed from subjects' MRI, interconnected based on geodesic distances, and scaled-up to current dipole densities based on laminar recordings in humans. MRIs were used to generate a quasi-static electromagnetic model enabling simulated cortical activity to be projected to the M/EEG sensors. RESULTS: The simulated M/EEG spindles were similar in amplitude and topography to empirical examples in the same subjects. Simulated spindles with more core-dominant activity were more MEG weighted. COMPARISON WITH EXISTING METHODS: Previous models lacked either spindle-generating thalamic neural dynamics or whole head biophysical modeling; the framework presented here is the first to simultaneously capture these disparate scales. CONCLUSIONS: This multiscale model provides a platform for the principled quantitative integration of existing information relevant to the generation of sleep spindles, and allows the implications of future findings to be explored. It provides a proof of principle for a methodological framework allowing large-scale integrative brain oscillations to be understood in terms of their underlying channels and synapses.

A procedure to increase the power of Granger-causal analysis through temporal smoothing.

BACKGROUND: How the human brain coordinates network activity to support cognition and behavior remains poorly understood. New high-resolution recording modalities facilitate a more detailed understanding of the human brain network. Several approaches have been proposed to infer functional networks, indicating the transient coordination of activity between brain regions, from neural time series. One category of approach is based on statistical modeling of time series recorded from multiple sensors (e.g., multivariate Granger causality). However, fitting such models remains computationally challenging as the history structure may be long in neural activity, requiring many model parameters to fully capture the dynamics. NEW METHOD: We develop a method based on Granger causality that makes the assumption that the history dependence varies smoothly. We fit multivariate autoregressive models such that the coefficients of the lagged history terms are smooth functions. We do so by modelling the history terms with a lower dimensional spline basis, which requires many fewer parameters than the standard approach and increases the statistical power of the model. RESULTS: We show that this procedure allows accurate estimation of brain dynamics and functional networks in simulations and examples of brain voltage activity recorded from a patient with pharmacoresistant epilepsy. COMPARISON WITH EXISTING METHOD: The proposed method has more statistical power than the Granger method for networks of signals that exhibit extended and smooth history dependencies. CONCLUSIONS: The proposed tool permits conditional inference of functional networks from many brain regions with extended history dependence, furthering the applicability of Granger causality to brain network science.

Human seizures couple across spatial scales through travelling wave dynamics.

Epilepsy-the propensity toward recurrent, unprovoked seizures-is a devastating disease affecting 65 million people worldwide. Understanding and treating this disease remains a challenge, as seizures manifest through mechanisms and features that span spatial and temporal scales. Here we address this challenge through the analysis and modelling of human brain voltage activity recorded simultaneously across microscopic and macroscopic spatial scales. We show that during seizure large-scale neural populations spanning centimetres of cortex coordinate with small neural groups spanning cortical columns, and provide evidence that rapidly propagating waves of activity underlie this increased inter-scale coupling. We develop a corresponding computational model to propose specific mechanisms-namely, the effects of an increased extracellular potassium concentration diffusing in space-that support the observed spatiotemporal dynamics. Understanding the multi-scale, spatiotemporal dynamics of human seizures-and connecting these dynamics to specific biological mechanisms-promises new insights to treat this devastating disease.

Epileptiform abnormalities predict delayed cerebral ischemia in subarachnoid hemorrhage.

OBJECTIVE: To identify whether abnormal neural activity, in the form of epileptiform discharges and rhythmic or periodic activity, which we term here ictal-interictal continuum abnormalities (IICAs), are associated with delayed cerebral ischemia (DCI). METHODS: Retrospective analysis of continuous electroencephalography (cEEG) reports and medical records from 124 patients with moderate to severe grade subarachnoid hemorrhage (SAH). We identified daily occurrence of seizures and IICAs. Using survival analysis methods, we estimated the cumulative probability of IICA onset time for patients with and without delayed cerebral ischemia (DCI). RESULTS: Our data suggest the presence of IICAs indeed increases the risk of developing DCI, especially when they begin several days after the onset of SAH. We found that all IICA types except generalized rhythmic delta activity occur more commonly in patients who develop DCI. In particular, IICAs that begin later in hospitalization correlate with increased risk of DCI. CONCLUSIONS: IICAs represent a new marker for identifying early patients at increased risk for DCI. Moreover, IICAs might contribute mechanistically to DCI and therefore represent a new potential target for intervention to prevent secondary cerebral injury following SAH. SIGNIFICANCE: These findings imply that IICAs may be a novel marker for predicting those at higher risk for DCI development.

IFPA meeting 2015 workshop report IV: placenta and obesity; stem cells of the feto-maternal interface; placental immunobiology and infection.

Workshops are an important part of the IFPA annual meeting as they allow for discussion of specialised topics. At the 2015 IFPA annual meeting there were 12 themed workshops, three of which are summarized in this report. These workshops related to various aspects of placental biology and collectively covered areas of obesity and the placenta, stem cells of the feto-maternal interface, and placental immunobiology and infection.

Rapid annotation of interictal epileptiform discharges via template matching under Dynamic Time Warping.

BACKGROUND: EEG interpretation relies on experts who are in short supply. There is a great need for automated pattern recognition systems to assist with interpretation. However, attempts to develop such systems have been limited by insufficient expert-annotated data. To address these issues, we developed a system named NeuroBrowser for EEG review and rapid waveform annotation. NEW METHODS: At the core of NeuroBrowser lies on ultrafast template matching under Dynamic Time Warping, which substantially accelerates the task of annotation. RESULTS: Our results demonstrate that NeuroBrowser can reduce the time required for annotation of interictal epileptiform discharges by EEG experts by 20-90%, with an average of approximately 70%. COMPARISON WITH EXISTING METHOD(S): In comparison with conventional manual EEG annotation, NeuroBrowser is able to save EEG experts approximately 70% on average of the time spent in annotating interictal epileptiform discharges. We have already extracted 19,000+ interictal epileptiform discharges from 100 patient EEG recordings. To our knowledge this represents the largest annotated database of interictal epileptiform discharges in existence. CONCLUSION: NeuroBrowser is an integrated system for rapid waveform annotation. While the algorithm is currently tailored to annotation of interictal epileptiform discharges in scalp EEG recordings, the concepts can be easily generalized to other waveforms and signal types.

EEG functional connectivity is partially predicted by underlying white matter connectivity.

Over the past decade, networks have become a leading model to illustrate both the anatomical relationships (structural networks) and the coupling of dynamic physiology (functional networks) linking separate brain regions. The relationship between these two levels of description remains incompletely understood and an area of intense research interest. In particular, it is unclear how cortical currents relate to underlying brain structural architecture. In addition, although theory suggests that brain communication is highly frequency dependent, how structural connections influence overlying functional connectivity in different frequency bands has not been previously explored. Here we relate functional networks inferred from statistical associations between source imaging of EEG activity and underlying cortico-cortical structural brain connectivity determined by probabilistic white matter tractography. We evaluate spontaneous fluctuating cortical brain activity over a long time scale (minutes) and relate inferred functional networks to underlying structural connectivity for broadband signals, as well as in seven distinct frequency bands. We find that cortical networks derived from source EEG estimates partially reflect both direct and indirect underlying white matter connectivity in all frequency bands evaluated. In addition, we find that when structural support is absent, functional connectivity is significantly reduced for high frequency bands compared to low frequency bands. The association between cortical currents and underlying white matter connectivity highlights the obligatory interdependence of functional and structural networks in the human brain. The increased dependence on structural support for the coupling of higher frequency brain rhythms provides new evidence for how underlying anatomy directly shapes emergent brain dynamics at fast time scales.

The maturation of cortical sleep rhythms and networks over early development.

OBJECTIVE: Although neuronal activity drives all aspects of cortical development, how human brain rhythms spontaneously mature remains an active area of research. We sought to systematically evaluate the emergence of human brain rhythms and functional cortical networks over early development. METHODS: We examined cortical rhythms and coupling patterns from birth through adolescence in a large cohort of healthy children (n=384) using scalp electroencephalogram (EEG) in the sleep state. RESULTS: We found that the emergence of brain rhythms follows a stereotyped sequence over early development. In general, higher frequencies increase in prominence with striking regional specificity throughout development. The coordination of these rhythmic activities across brain regions follows a general pattern of maturation in which broadly distributed networks of low-frequency oscillations increase in density while networks of high frequency oscillations become sparser and more highly clustered. CONCLUSION: Our results indicate that a predictable program directs the development of key rhythmic components and physiological brain networks over early development. SIGNIFICANCE: This work expands our knowledge of normal cortical development. The stereotyped neurophysiological processes observed at the level of rhythms and networks may provide a scaffolding to support critical periods of cognitive growth. Furthermore, these conserved patterns could provide a sensitive biomarker for cortical health across development.

Intracellular patterns of sialophorin expression define a new molecular classification of breast cancer and represent new targets for therapy.

BACKGROUND: Sialophorin is a transmembrane sialoglycoprotein. Normally, the molecule is only produced by white blood cells where it regulates functions such as intercellular adhesion, intracellular signalling, apoptosis, migration and proliferation. METHODS: Normal breast tissue and primary breast tumours were analysed by immunohistochemistry for sialophorin expression. The sialophorin-positive breast cancer cell line MCF7 was engineered to stably express either non-targeted or sialophorin-targeted small interfering RNA (siRNA). Assays were then performed in vitro to assess apoptosis, intracellular adhesion, transendothelial migration and cytotoxicity. An orthotopic mouse model assayed ability to produce tumours in vivo. RESULTS: Normal breast epithelial cells exhibit expression of the N-terminal domain of sialophorin in the cytoplasm but not the nucleus. The majority of these normal cells are also negative for expression of the C-terminal domain. In contrast, malignant breast epithelial cells exhibit N-terminal expression both in the cytoplasm and nucleus and the majority express the C-terminus in the nucleus. Using differential patterns of intracellular expression of the N and C termini of sialophorin, we define six subtypes of breast cancer that are independent of histological and receptor status classification. Targeting sialophorin with siRNA resulted in the MCF7 breast cancer cell line exhibiting increased homotypic adhesion, decreased transendothelial migration, increased susceptibility to apoptosis, increased vulnerability to lysis by natural killer cells and decreased ability to produce tumours in mice. CONCLUSION: Our results indicate that intracellular patterns of sialophorin expression define a new molecular classification of breast cancer and that sialophorin represents a novel therapeutic target.

Consideration of epilepsy surgery in adults should be independent of age.

OBJECTIVES: Epilepsy surgery is performed less frequently in persons over 45 years of age than in younger individuals, probably reflecting biases among patients, referring physicians and neurologists. METHODS: We report on a clinically heterogeneous cohort of patients aged 45 years or older who underwent epilepsy surgery for medically intractable epilepsy. RESULTS: Over a 15-year period, 42 patients with a mean duration of epilepsy of 27.3 years underwent elective surgery. The mean follow-up period was 48 months. Thirty-two patients had an Engel class I outcome, of which 23 were totally seizure-free (Ia). Six patients had a class II outcome (rare disabling seizures), one had a class III outcome (worthwhile improvement), and three had a class IV outcome (no worthwhile improvement). The majority of patients reported an improved quality of life and satisfaction with the epilepsy surgery. A subjective improvement in cognition was reported in 7 patients while a decline was reported in 10 patients. New neuropsychiatric difficulties were reported in three patients while three patients reported improved anxiety after surgery. Only one patient became newly employed after surgery while 23 returned to driving. Permanent complications occurred in four patients (thalamic infarct during a Wada test (n=1) and asymptomatic visual field defect (n=3)). CONCLUSIONS: We report a favorable outcome from epilepsy surgery in a large series of older adults and conclude that age per se is not a contraindication to epilepsy surgery. We emphasize the lack of correlation between outcome from surgery and pre-operative duration of epilepsy.

Influence of health and environmental information on hedonic evaluation of organic and conventional bread.

Grain from paired samples of the hard red spring wheat cultivar "Park" grown on both conventionally and organically managed land was milled and baked into 60% whole wheat bread. Consumers (n= 384) rated their liking of the bread samples on a 9-point hedonic scale before (blind) and after (labeled) receiving information about organic production. Consumers liked organic bread more (P < 0.05) than conventional bread under blind and labeled conditions. Environmental information about organic production did not impact consumer preference changes for organic bread, but health information coupled with sensory evaluation increased liking of organic bread. Ordinary least squares (OLS) and binary response (probit) regression models identified that postsecondary education, income level, frequency of bread consumption, and proenvironmental attitudes played a significant role in preference changes for organic bread. The techniques used in this study demonstrate that a combination of sensory and econometric techniques strengthens the evaluation of consumer food choice.

Pseudosyndactyly and musculoskeletal contractures in inherited epidermolysis bullosa: experience of the National Epidermolysis Bullosa Registry, 1986-2002.

Mitten deformities of the hands and feet occur in nearly every patient with the most severe subtype (Hallopeau-Siemens) of recessive dystrophic epidermolysis bullosa, and in at least 40-50% of all other recessive dystrophic epidermolysis bullosa patients. Smaller numbers of patients with dominant dystrophic, junctional, and simplex types of epidermolysis bullosa are also at risk of this complication. Surgical intervention is commonly performed to correct these deformities, but recurrence and the need for repeated surgery are common. Higher numbers of epidermolysis bullosa patients also develop musculoskeletal contractures in other anatomic sites, further impairing overall function. Lifetable analyses not only better project the cumulative risk of mitten deformities and other contractures but also emphasize the need for early surveillance and intervention, since both of these musculoskeletal complications may occur within the first year of life.

Mechanisms of calcium influx into hippocampal spines: heterogeneity among spines, coincidence detection by NMDA receptors, and optical quantal analysis.

Dendritic spines receive most excitatory inputs in the vertebrate brain, but their function is still poorly understood. Using two-photon calcium imaging of CA1 pyramidal neurons in rat hippocampal slices, we investigated the mechanisms by which calcium enters into individual spines in the stratum radiatum. We find three different pathways for calcium influx: high-threshold voltage-sensitive calcium channels, NMDA receptors, and an APV-resistant influx consistent with calcium-permeable AMPA or kainate receptors. These pathways vary among different populations of spines and are engaged under different stimulation conditions, with peak calcium concentrations reaching >10 microM. Furthermore, as a result of the biophysical properties of the NMDA receptor, the calcium dynamics of spines are exquisitely sensitive to the temporal coincidence of the input and output of the neuron. Our results confirm that individual spines are chemical compartments that can perform coincidence detection. Finally, we demonstrate that functional studies and optical quantal analysis of single, identified synapses is feasible in mammalian CNS neurons in brain slices.

Linear summation of excitatory inputs by CA1 pyramidal neurons.

A fundamental problem in neurobiology is understanding the arithmetic that dendrites use to integrate inputs. The impact of dendritic morphology and active conductances on input summation is still unknown. To study this, we use glutamate iontophoresis and synaptic stimulation to position pairs of excitatory inputs throughout the apical, oblique, and basal dendrites of CA1 pyramidal neurons in rat hippocampal slices. Under a variety of stimulation regimes, we find a linear summation of most input combinations that is implemented by a surprising balance of boosting and shunting mechanisms. Active conductances in dendrites paradoxically serve to make summation linear. This "active linearity" can reconcile predictions from cable theory with the observed linear summation in vivo and suggests that a simple arithmetic is used by apparently complex dendritic trees.

Input summation by cultured pyramidal neurons is linear and position-independent.

The role of dendritic morphology in integration and processing of neuronal inputs is still unknown. Models based on passive cable theory suggest that dendrites serve to isolate synapses from one another. Because of decreases in driving force or resistance, two inputs onto the same dendrite would diminish their joint effect, resulting in sublinear summation. When on different dendrites, however, inputs would not interact and therefore would sum linearly. These predictions have not been rigorously tested experimentally. In addition, recent results indicate that dendrites have voltage-sensitive conductances and are not passive cables. To investigate input integration, we characterized the effects of dendritic morphology on the summation of subthreshold excitatory inputs on cultured hippocampal neurons with pyramidal morphologies. We used microiontophoresis of glutamate to systematically position inputs throughout the dendritic tree and tested the summation of two inputs by measuring their individual and joint effects. We find that summation was surprisingly linear regardless of input position. For small inputs, this linearity arose because no significant shunts or changes in driving force occurred and no voltage-dependent channels were opened. Larger inputs also added linearly, but this linearity was caused by balanced action of NMDA and IA potassium conductances. Therefore, active conductances can maintain, paradoxically, a linear input arithmetic. Furthermore, dendritic morphology does not interfere with this linearity, which may be essential for particular neuronal computations.

Quantal neurotransmitter secretion rate exhibits fractal behavior.

The rate of exocytic events from both neurons and non-neuronal cells exhibits fluctuations consistent with fractal (self-similar) behavior in time, as evidenced by a number of statistical measures. We explicitly demonstrate this for neurotransmitter secretion at Xenopus neuromuscular junctions and for rat hippocampal synapses in culture; the exocytosis of exogenously supplied neurotransmitter from cultured Xenopus myocytes and from rat fibroblasts behaves similarly. The magnitude of the fluctuations of the rate of exocytic events about the mean decreases slowly as the rate is computed over longer and longer time periods, the periodogram decreases in power-law manner with frequency, and the Allan factor (relative variance of the number of exocytic events) increases as a power-law function of the counting time. These features are hallmarks of self-similar behavior. Their description requires models that exhibit long-range correlation (memory) in event occurrences. We have developed a physiologically plausible model that accords with all of the statistical measures that we have examined. The appearance of fractal behavior at synapses, as well as in systems comprising collections of synapses, indicates that such behavior is ubiquitous in neural signaling.