Spatial Topographies Research Articles

Reduced Cortical Excitability is Associated with Cognitive Symptoms in Concussed Adolescent Football Players.

American tackle football is associated with high rates of concussion, leading to neurophysiological disturbances and debilitating clinical symptoms. Previous investigations of the neurophysiological effects of concussion have largely ignored aperiodic neurophysiological activity, which is a marker of cortical excitability. We examined whether concussion during a season of high school football is related to changes in aperiodic and periodic neurophysiological activity and whether any such changes are associated with clinical outcomes. Pre- and post-season resting-state magnetoencephalography (MEG) data were collected from 91 high school football players over as many as four seasons of play, for a total of 278 data collections. During these seasons of football play, a cohort of 10 individuals were diagnosed with concussion. MEG data were source-imaged, frequency-transformed and parameterized, and linear mixed models were used to examine effects of concussion on pre-to-post-season changes in neurophysiological activity. Scores on the Post-Concussive Symptom Inventory were correlated with pre-to-post-season neurophysiological changes to determine their clinical relevance. Concussion was associated with increased aperiodic exponents in superior frontal cortices, indicating a relative reduction in cortical excitability. This slowing of aperiodic neurophysiology mediated concussion effects on raw delta and gamma power and was associated with worse cognitive concerns across participants. Pre-to-post-season changes in aperiodic-corrected alpha and theta rhythmic activity were also decreased in posterior cortices in concussed players. These findings indicate that concussion alters both the excitability and rhythmic signaling of the cortex, with differing spatial topographies and implications for clinical symptoms.

Karsty and miserable: Dark humor in the subsurface geopoetics of caving

Geographic accounts of caving have critiqued the propensity of affects and practices which reinforce EuroAmerican coloniality and hubris, while also finding alternative paths of intimacy and lively relationality. Yet subsurface geographies can also reinforce cliched spatial and moral topographies. This article reflects on the production of an ambivalent affect of “dark humor” in caves, through caver social worlds, and in cave writing and song. I use a Bachelardian phenomenology of the imagination as a method of interpreting the geopoetics of cave space and cave emotion related topologically rather than topographically into easy interior/exterior or surface/depth oppositions. Through Jan and Herb Conn's cave ballads and poetry, imaginative place names, and accounts of irritating struggles with manganese, a different emotional culture of caving is produced than the masculine risk-seeking one might expect. Nonetheless, such phenomenological focus on immediacy of creative imagination risks precluding broader historical-political contexts of settler coloniality. Seeking a position between “ruthlessly critical” or “post-critically evasive” analyses, I consider how the Conns produced an “anti-heroic” attitude useful beyond caving and the politics of the subsurface but without purity or innocence.

Levitational 3D Bioassembly and Density‐Based Spatial Coding of Levitoids

AbstractSelf‐assembly of cells into functional bioarchitectures with microscale spatial topographies are prevalent in nature. Despite the advances to recapitulate the native‐like microenvironment in vitro,fabrication of soft living materials with a deterministic control on the composition, functionality, and geometry, is still challenging. Here, a versatile, paramagnetically tunable, levitational biofabrication technique within a ring‐magnet system, RM‐LEV, is developed to assemble single cells or heterogeneous multicellular living architectures in a scaffold‐free manner. The self‐assembly and contactless biofabrication of multiple cell types into 3D multicellular “levitospheres” is demonstrated, where cellular positions are guided by levitation and density, simultaneously. Inherent density and diamagnetism of different cell types are leveraged to manipulate the geometry and self‐assembly of levitospheres into larger complex 3D structures, termed as “levitoids”. This approach is further applied to manipulate, position, and culture levitoids within a 3D hydrogel matrix under levitation, which preserves their viability and functionality. Thus, this study provides a foundation to create spatially heterogeneous 3D cellular assemblies with density‐coded bio‐architectures—which is not previously realized using magnetic levitation. This density‐based coding approach can be beneficial to study the cross‐talk between different cell types within spheroids and organoids, and be broadly applied to 3D bioprinting, tissue engineering, cancer, and neuroscience research.

A novel brain-computer interface based on audio-assisted visual evoked EEG and spatial-temporal attention CNN.

ObjectiveBrain-computer interface (BCI) can translate intentions directly into instructions and greatly improve the interaction experience for disabled people or some specific interactive applications. To improve the efficiency of BCI, the objective of this study is to explore the feasibility of an audio-assisted visual BCI speller and a deep learning-based single-trial event related potentials (ERP) decoding strategy.ApproachIn this study, a two-stage BCI speller combining the motion-onset visual evoked potential (mVEP) and semantically congruent audio evoked ERP was designed to output the target characters. In the first stage, the different group of characters were presented in the different locations of visual field simultaneously and the stimuli were coded to the mVEP based on a new space division multiple access scheme. And then, the target character can be output based on the audio-assisted mVEP in the second stage. Meanwhile, a spatial-temporal attention-based convolutional neural network (STA-CNN) was proposed to recognize the single-trial ERP components. The CNN can learn 2-dimentional features including the spatial information of different activated channels and time dependence among ERP components. In addition, the STA mechanism can enhance the discriminative event-related features by adaptively learning probability weights.Main resultsThe performance of the proposed two-stage audio-assisted visual BCI paradigm and STA-CNN model was evaluated using the Electroencephalogram (EEG) recorded from 10 subjects. The average classification accuracy of proposed STA-CNN can reach 59.6 and 77.7% for the first and second stages, which were always significantly higher than those of the comparison methods (p < 0.05).SignificanceThe proposed two-stage audio-assisted visual paradigm showed a great potential to be used to BCI speller. Moreover, through the analysis of the attention weights from time sequence and spatial topographies, it was proved that STA-CNN could effectively extract interpretable spatiotemporal EEG features.

Transient brain-wide coactivations and structured transitions revealed in hemodynamic imaging data

Brain-wide patterns in resting human brains, as either structured functional connectivity (FC) or recurring brain states, have been widely studied in the neuroimaging literature. In particular, resting-state FCs estimated over windowed timeframe neuroimaging data from sub-minutes to minutes using correlation or blind source separation techniques have reported many brain-wide patterns of significant behavioral and disease correlates. The present pilot study utilized a novel whole-head cap-based high-density diffuse optical tomography (DOT) technology, together with data-driven analysis methods, to investigate recurring transient brain-wide patterns in spontaneous fluctuations of hemodynamic signals at the resolution of single timeframes from thirteen healthy adults in resting conditions. Our results report that a small number, i.e., six, of brain-wide coactivation patterns (CAPs) describe major spatiotemporal dynamics of spontaneous hemodynamic signals recorded by DOT. These CAPs represent recurring brain states, showing spatial topographies of hemispheric symmetry, and exhibit highly anticorrelated pairs. Moreover, a structured transition pattern among the six brain states is identified, where two CAPs with anterior-posterior spatial patterns are significantly involved in transitions among all brain states. Our results further elucidate two brain states of global positive and negative patterns, indicating transient neuronal coactivations and co-deactivations, respectively, over the entire cortex. We demonstrate that these two brain states are responsible for the generation of a subset of peaks and troughs in global signals (GS), supporting the recent reports on neuronal relevance of hemodynamic GS. Collectively, our results suggest that transient neuronal events (i.e., CAPs), global brain activity, and brain-wide structured transitions co-exist in humans and these phenomena are closely related, which extend the observations of similar neuronal events recently reported in animal hemodynamic data. Future studies on the quantitative relationship among these transient events and their relationships to windowed FCs along with larger sample size are needed to understand their changes with behaviors and diseased conditions.

Laser Direct Writing of Dual-Scale 3D Structures for Cell Repelling at High Cellular Density.

The fabrication of complex, reproducible, and accurate micro-and nanostructured interfaces that impede the interaction between material’s surface and different cell types represents an important objective in the development of medical devices. This can be achieved by topographical means such as dual-scale structures, mainly represented by microstructures with surface nanopatterning. Fabrication via laser irradiation of materials seems promising. However, laser-assisted fabrication of dual-scale structures, i.e., ripples relies on stochastic processes deriving from laser–matter interaction, limiting the control over the structures’ topography. In this paper, we report on laser fabrication of cell-repellent dual-scale 3D structures with fully reproducible and high spatial accuracy topographies. Structures were designed as micrometric “mushrooms” decorated with fingerprint-like nanometric features with heights and periodicities close to those of the calamistrum, i.e., 200–300 nm. They were fabricated by Laser Direct Writing via Two-Photon Polymerization of IP-Dip photoresist. Design and laser writing parameters were optimized for conferring cell-repellent properties to the structures, even for high cellular densities in the culture medium. The structures were most efficient in repelling the cells when the fingerprint-like features had periodicities and heights of ≅200 nm, fairly close to the repellent surfaces of the calamistrum. Laser power was the most important parameter for the optimization protocol.

A deep learning method for single-trial EEG classification in RSVP task based on spatiotemporal features of ERPs

Objective. Single-trial electroencephalography (EEG) classification is of great importance in the rapid serial visual presentation (RSVP) task. Convolutional neural networks (CNNs), as one of the mainstream deep learning methods, have been proven to be effective in extracting RSVP EEG features. However, most existing CNN models for EEG classification do not consider the phase-locked characteristic of event-related potential (ERP) components very well in the architecture design. Here, we propose a novel CNN model to make better use of the phase-locked characteristic to extract spatiotemporal features for single-trial RSVP EEG classification. Based on the phase-locked characteristic, the spatial distributions of the main ERP component in different periods can be learned separately. Approach. In this work, we propose a novel CNN model to achieve superior performance on single-trial RSVP EEG classification. We introduce the combination of the standard convolutional layer, the permute layer and the depthwise convolutional layer to separately operate the spatial convolution in different periods, which more fully utilizes the phase-locked characteristic of ERPs for classification. We compare our model with several traditional and deep-learning methods in the classification performance. Moreover, we use spatial topography and saliency map to visually analyze the ERP features extracted by our model. Main results. The results show that our model obtains better classification performance than those of reference methods. The spatial topographies of each subject exhibit the typical ERP spatial distribution in different time periods. And the saliency map of each subject illustrates the discriminant electrodes and the meaningful temporal features. Significance. Our model is designed with better consideration of the phase-locked ERP characteristic and reaches excellent performance on single-trial RSVP EEG classification.

Computational Models in Electroencephalography

Computational models lie at the intersection of basic neuroscience and healthcare applications because they allow researchers to test hypotheses in silico and predict the outcome of experiments and interactions that are very hard to test in reality. Yet, what is meant by “computational model” is understood in many different ways by researchers in different fields of neuroscience and psychology, hindering communication and collaboration. In this review, we point out the state of the art of computational modeling in Electroencephalography (EEG) and outline how these models can be used to integrate findings from electrophysiology, network-level models, and behavior. On the one hand, computational models serve to investigate the mechanisms that generate brain activity, for example measured with EEG, such as the transient emergence of oscillations at different frequency bands and/or with different spatial topographies. On the other hand, computational models serve to design experiments and test hypotheses in silico. The final purpose of computational models of EEG is to obtain a comprehensive understanding of the mechanisms that underlie the EEG signal. This is crucial for an accurate interpretation of EEG measurements that may ultimately serve in the development of novel clinical applications.

A High-Dimensional Window into the Micro-Environment of Triple Negative Breast Cancer.

Simple SummaryTriple negative breast cancer (TNBC) shows a substantial level of genomic, cellular, and phenotypic heterogeneity. While genomic heterogeneity and subclonal diversity are prevalent in this subgroup of tumors, a growing body of evidence indicates that the disease course depends on the interaction between cancer cells and the tumor micro-environment (TME). The TME is not static and can change over time, owing to differences in cell numbers, phenotypes, and spatial relationships. Efforts to further elucidate the TME have been aided by a plethora of new technologies that study tumors in a high-dimensional manner. These high-dimensional technologies enable comprehensive analysis of cell phenotypes at the single cell level or the spatial relationships of tumor and immune cells. In this review, we discuss studies in TNBC that unravel specific spatial patterns of cells in the breast TME and single cell phenotypes.Providing effective personalized immunotherapy for triple negative breast cancer (TNBC) patients requires a detailed understanding of the composition of the tumor microenvironment. Both the tumor cell and non-tumor components of TNBC can exhibit tremendous heterogeneity in individual patients and change over time. Delineating cellular phenotypes and spatial topographies associated with distinct immunological states and the impact of chemotherapy will be necessary to optimally time immunotherapy. The clinical successes in immunotherapy have intensified research on the tumor microenvironment, aided by a plethora of high-dimensional technologies to define cellular phenotypes. These high-dimensional technologies include, but are not limited to, single cell RNA sequencing, spatial transcriptomics, T cell repertoire analyses, advanced flow cytometry, imaging mass cytometry, and their integration. In this review, we discuss the cellular phenotypes and spatial patterns of the lymphoid-, myeloid-, and stromal cells in the TNBC microenvironment and the potential value of mapping these features onto tumor cell genotypes.

EEG microstates associated with intra- and inter-subject alpha variability

Variation of the magnitude of posterior alpha rhythm (8–12 Hz) has functional and behavioural effects in sensory processing and cognitive performances. Electrical brain activity, as revealed by electroencephalography (EEG), can be represented by a sequence of microstates of about 40–120 ms duration, in which distributed neural pools are synchronously active and generate stable spatial potential topographies on the scalp. Microstate dynamics may reflect transitions between global states characterized by selective inhibition of specific intra-cortical regions, mediated by alpha activity. We investigated the intra-subject and inter-subject relationship between microstate features and alpha band. High-density EEG signals were acquired in 29 healthy subjects during ten minutes of eyes closed rest. Individual EEG signal epochs were classified into four groups depending on the amount of occipital alpha power, and microstate metrics (duration, coverage and frequency of occurrence) were calculated and compared across groups. Correlations between alpha power and microstate metrics between individuals were also performed. To assess if microstate parameter variations are specific for the alpha band, the same analysis was also performed for theta and beta bands, as well as for global field power. We observed an increase in the metrics of microstate, previously associated to the visual system, with the level of intra-subject amplitude alpha oscillations, together with lower coverage of microstate associated with executive attention network and a higher frequency of microstate associated with task negative network. Other modulation effects of broad-band EEG power level on microstate metrics were observed. These effects are not specific for the alpha band, since they can equally be attributed to fluctuations in other frequency bands. We can interpret our results as a regulation mechanism mediated by posterior alpha level, dynamically interacting with other frequency bands, responsible for the switching between active areas.

Towards a Universal Taxonomy of Macro-scale Functional Human Brain Networks.

The past decade has witnessed a proliferation of studies aimed at characterizing the human connectome. These projects map the brain regions comprising large-scale systems underlying cognition using non-invasive neuroimaging approaches and advanced analytic techniques adopted from network science. While the idea that the human brain is composed of multiple macro-scale functional networks has been gaining traction in cognitive neuroscience, the field has yet to reach consensus on several key issues regarding terminology. What constitutes a functional brain network? Are there "core" functional networks, and if so, what are their spatial topographies? What naming conventions, if universally adopted, will provide the most utility and facilitate communication amongst researchers? Can a taxonomy of functional brain networks be delineated? Here we survey the current landscape to identify six common macro-scale brain network naming schemes and conventions utilized in the literature, highlighting inconsistencies and points of confusion where appropriate. As a minimum recommendation upon which to build, we propose that a scheme incorporating anatomical terminology should provide the foundation for a taxonomy of functional brain networks. A logical starting point in this endeavor might delineate systems that we refer to here as "occipital", "pericentral", "dorsal frontoparietal", "lateral frontoparietal", "midcingulo-insular", and "medial frontoparietal" networks. We posit that as the field of network neuroscience matures, it will become increasingly imperative to arrive at a taxonomy such as that proposed here, that can be consistently referenced across research groups.

A Comparative Study of Different EEG Reference Choices for Event-Related Potentials Extracted by Independent Component Analysis.

In the event-related potential (ERP) of scalp electroencephalography (EEG) studies, the vertex reference (Cz), linked mastoids or ears (LM), and average reference (AVG) are popular reference methods, and the reference electrode standardization technique (REST) is increasingly applied. Because scalp EEG recordings are considered as spatially degraded signals, independent component analysis (ICA) is a widely used data-driven method for obtaining ERPs by decomposing EEG data. However, the accurate estimation of the differences in ERP components extracted by ICA with different references remains unclear. In this study, we first provided formal descriptions of the above reference methods (Cz, LM, AVG, and REST) and ICA decomposition in ERP and then investigated the influences of different reference techniques on simulation and real EEG datasets. The results revealed that (1) the reference method did not change the peak amplitudes and latencies of relative ERPs corresponding to some IC time courses; (2) there were non-negligible effects of different reference methods on both temporal ERPs and spatial topographies of some ICs; and (3) compared to Cz, LM, and AR, considering both the performances of temporal ERPs and spatial topographies, the REST reference had overall superiority. These findings provide a recommended choice of REST for ICA analysis at the trial level and contribute to empirical investigations regarding the use of reference methods in ERP domains with ICA analysis.

90. Computational Modeling of Topographic Variation in Microcircuitry Across Human Cortex: Toward Linking Molecular Alterations With Neuroimaging Biomarkers

Neuroimaging biomarkers for neuropsychiatric disorders and pharmacology exhibit spatial topographies, i.e., structured patterns across brain regions. A key computational challenge is to understand how such neuroimaging patterns emerge from underlying alterations in microcircuit function. Furthermore, development of pharmacological therapeutics may benefit from linking macro-circuit characterizations of neurobehavioral processes and pathologies to heterogeneous topographies of drug targets; this circuit-based approach can evaluate systemic pharmacological effects on "on-target" vs. “off-target” brain regions.

Characterization of infant mu rhythm immediately before crawling: A high-resolution EEG study

Crawling is an important milestone in infant motor development. However, infants with developmental motor disorders can exhibit delays, or even miss, in the acquisition of crawling skill. And little information is available from the neurodevelopmental domain about the changes in brain function with intervention. The mu rhythm can potentially play a substantial role in understanding human motor development at early ages in infants, as it has in adults. Studies about the mu rhythm in infants were in coarse temporal resolution with longitudinal samples taken months or years apart. Details about the infant mu rhythm at a fine age resolution has not been fully revealed, which leads to contradictory evidence about its formulation and developmental changes of its spectral origins and, therefore, impedes the full understanding of motor brain development before crawling skill acquisition. The present study aims to expand knowledge about the infant mu rhythm and its spatio-spectral pattern shifts along maturation immediately before crawling. With high-density EEG data recorded on a weekly basis and simultaneous characterization of spatio-spectral patterns of the mu rhythm, subtle developmental changes in its spectral peak, frequency range, and scalp topography are revealed. This mu rhythm further indicates a significant correlation to the crawling onset while powers from other frequency bands do not show such correlations. These details of developmental changes about the mu rhythm provide an insight of rapid changes in the human motor cortex in the first year of life. Our results are consistent with previous findings about the peak frequency shifting of the mu rhythm and further depict detailed developmental curves of its frequency ranges and spatial topographies. The infant mu rhythm could potentially be used to assess motor brain deficiencies at early ages and to evaluate intervention effectiveness in children with neuromotor disorders.

Frequency shift in topography of spontaneous brain rhythms from childhood to adulthood.

It has been described that the frequency ranges at which theta, mu and alpha rhythms oscillate is increasing with age. The present report, by analyzing the spontaneous EEG, tries to demonstrate whether there is an increase with age in the frequency at which the cortical structures oscillate. A topographical approach was followed. The spontaneous EEG of one hundredand seventy subjects was recorded. The spectral power (from 0.5 to 45.5Hz) was obtained by means of the Fast Fourier Transform. Correlations of spatial topographies among the different age groups showed that older groups presented the same topographical maps as younger groups, but oscillating at higher frequencies. The results suggest that the same brain areas oscillate at lower frequencies in children than in older groups, for a broad frequency range. This shift to a higher frequency with age would be a trend in spontaneous brain rhythm development.

Spatial Jurisdictions, Historical Topographies, and Sovereignty at the Leupp Isolation Center

Spatial Jurisdictions, Historical Topographies, and Sovereignty at the Leupp Isolation Center

Making Clear the Value of Basic Behavioral Research. Commentary: A Crisis in Comparative Psychology: Where Have All the Undergraduates Gone?

We agree with Abramson (2015) that the field of comparative psychology faces a problem with respect to student recruitment. We would argue, however, that the problem is much broader and encompasses all domains of basic behavioral research, of which comparative psychology is just one. Why is this? Abramson (2015) suggests two primary drivers are the lack of undergraduate courses and the lack of graduate programs on comparative psychology. We would add to this a lack of funding. In fact, based on the current funding climate, it is possible that the few undergraduate and graduate programs that exist will disappear. Why is the funding drying up? One need look not too long ago to the furore created by Congress who challenged the validity of the National Institute of Health's (NIH) peer-review process by singling out a peer-reviewed grant by Professor Edward Wasserman on pigeon's categorization abilities. According to a press release by the office of Rep. Randy Neugebauer (R-Texas), who introduced an amendment that would effectively by-pass NIH's peer-review process, “NIH has continually failed to give a high priority to research on serious mental illnesses.” That may have been true, but obviously Neugebauer felt he would get more traction for his cause by singling out behavioral work done with pigeons for ridicule. And that strategy is borne out of a failure on our part to explain why basic behavioral research is so important. As Abramson (2015) notes, to improve recruitment, we must make the connection between comparative psychology and human behavior clear. We would add that we must make it clear to students and funding agencies that basic behavioral research lies at the core of every study that seeks to understand human behavior, whether comparative in nature or not. Indeed, behavioral researchers are responsible for the majority of paradigms neuroscientists utilize to study the neural mechanisms of learning and memory. Further, a foundation in behavioral research places students in a strong position to tackle any number of topics in the future. Both authors of this commentary received their PhD training in basic behavioral (animal cognition) research. We both continue to conduct animal cognition studies, but one of us (MC) has embarked on a neuroscience career while the other (DS) has gone on to tackle topics from moral nativism (Scarf et al., 2012a,b) to alcohol consumption in university students (Riordan et al., 2015a,b). Don't get us wrong. We don't believe that basic behavioral research should exist to merely serve neuroscience, or that students should simply use their behavioral training as a springboard into other domains. Most (hopefully all) of us that conduct basic behavioral research do so because we find understanding human and nonhuman animal behavior fascinating. But as Neugebauer's comments foretell, the argument that understanding animal behavior is fascinating in its own right is becoming a harder and harder point to sell. The nuanced and elegant study conducted by Frank and Wasserman (2005) that finally revealed the equivalence relation of symmetry in nonhuman animals by using a successive go/no-go procedure to control for both spatial and temporal response topographies is nothing short of brilliant, but it will go unappreciated by most outside the field of basic behavioral research. Yet because of this study we are now one step closer to understanding the neural basis of equivalence relations, which underlie many of our complex behaviors (Sidman, 1994). In conclusion, it is very obvious to us why we need basic behavioral research. But we need to do a far better job of explaining to those outside our field and funding agencies why basic behavioral research is essential. Most importantly, it is critical that we present a unified front.

Spatial Topographies of Unilateral Subthalamic Nucleus Deep Brain Stimulation Efficacy for Ipsilateral, Contralateral, Midline, and Total Parkinson Disease Motor Symptoms

Subthalamic nucleus (STN) deep brain stimulation is a successful intervention for medically refractory Parkinson disease, although its efficacy depends on optimal electrode placement. Even though the predominant effect is observed contralaterally, modest improvements in ipsilateral and midline symptoms are also observed. To elucidate the role of contact location of unilateral deep brain stimulation on contralateral, ipsilateral, and axial subscores of Parkinson disease motor symptoms. Eighty-six patients receiving first deep brain stimulation STN electrode placements were identified, yielding 73 patients with 3-month follow-up. Total preoperative and postoperative Unified Parkinson Disease Rating Scale Part III scores were obtained and divided into contralateral, ipsilateral, and midline subscores. Contact location was determined on immediate postoperative magnetic resonance imaging. A 3-dimensional ordinary "kriging" algorithm generated spatial interpolations for total, ipsilateral, contralateral, and midline symptom categories. Interpolative reconstructions were performed in the axial planes (z = -0.5, -1.0, -1.5, -3.5, -4.5, -6.0) and a sagittal plane (x = 12.0). Interpolation error and significance were quantified by use of a cross-validation technique and quantile-quantile analysis. There was an overall reduction in Unified Parkinson Disease Rating Scale Part III symptoms: total = 37.0 ± 24.11% (P < .05), ipsilateral = 15.9 ± 51.8%, contralateral = 56.2 ± 26.8% (P < .05), and midline = 26.5 ± 34.7%. Kriging interpolation was performed and cross-validated with quantile-quantile analysis with high correlation (R2 > 0.92) and demonstrated regions of efficacy for each symptom category. Contralateral symptoms demonstrated broad regions of efficacy across the peri-STN area. The ipsilateral and midline regions of efficacy were constrained and located along the dorsal STN and caudal zona incerta. We provide evidence for a unique functional topographic window in which contralateral, ipsilateral, and midline structures may achieve the best efficacy. Although there are overlapping regions, laterality demonstrates distinct topographies. Surgical optimization should target the intersection of optimal regions for these symptom categories.

Segregation and the Social Relations of Place, Bombay, 1890–1910

For many, a defining feature of the colonial Indian city is the high rate of segregation of its European and Indians residents. Building on recent work that explores the messy realities of social and spatial relations, this paper argues that the social geographies of the colonial city were built on the social relations of place—the network of social relations that are bound up with a particular material setting. The result was that social spaces were centred on overlapping geographic patterns, intense negotiations over space, and heterogeneous lived-in spaces. In this paper, these points are examined through the case of Bombay's Modern Town at the turn of the twentieth century. Modern Town was supposedly the home of the city's European population and it stood in sharp contrast to the city's Native Town. However, Modern Town did not conform to the spatial topographies described by most writers. While formal residential segregation existed, social and economic articulation and interaction ensured that the district's social spaces were meshed in differentiated and complex ways.

Abnormal network topographies and changes in global activity: absence of a causal relationship.

Changes in regional brain activity can be observed following global normalization procedures to reduce variability in the data. In particular, spurious regional differences may appear when scans from patients with low global activity are compared to those from healthy subjects. It has thus been suggested that the consistent increases in subcortical activity that characterize the abnormal Parkinson's disease-related metabolic covariance pattern (PDRP) are artifacts of global normalization, and that similar topographies can be identified in scans from healthy subjects with varying global activity. To address this issue, we examined the effects of experimental reductions in global metabolic activity on PDRP expression. Ten healthy subjects underwent ¹⁸F-fluorodeoxyglucose PET in wakefulness and following sleep induction. In all subjects, the global metabolic rate (GMR) declined with sleep (mean -34%, range: -17 to -56%), exceeding the test-retest differences of the measure (p<0.001). By contrast, sleep-wake differences in PDRP expression did not differ from test-retest differences, and did not correlate (R²=0.04) with concurrent declines in global metabolic activity. Indeed, despite significant GMR reductions in sleep, PDRP values remained within the normal range. Likewise, voxel weights on the principal component patterns resulting from combined analysis of the sleep and wake scans did not correlate (R²<0.07) with the corresponding regional loadings on the PDRP topography. In aggregate, the data demonstrate that abnormal PDRP expression is not induced by reductions in global activity. Moreover, significant declines in GMR are not associated with the appearance of PDRP-like spatial topographies.