Structural Connectivity Metrics Research Articles

Characterizing normal perinatal development of the human brain structural connectivity.

Early brain development is characterized by the formation of a highly organized structural connectome, which underlies brain's cognitive abilities and influences its response to diseases and environmental factors. Hence, quantitative assessment of structural connectivity in the perinatal stage is useful for studying normal and abnormal neurodevelopment. However, estimation of the connectome from diffusion MRI data involves complex computations. For the perinatal period, these computations are further challenged by the rapid brain development, inherently low signal quality, imaging difficulties, and high inter-subject variability. These factors make it difficult to chart the normal development of the structural connectome. As a result, there is a lack of reliable normative baselines of structural connectivity metrics at this critical stage in brain development. In this study, we developed a computational method based on spatio-temporal averaging in the image space for determining such baselines. We used this method to analyze the structural connectivity between 33 and 44 postmenstrual weeks using data from 166 subjects. Our results unveiled clear and strong trends in the development of structural connectivity in the perinatal stage. We observed increases in measures of network integration and segregation, and widespread strengthening of the connections within and across brain lobes and hemispheres. We also observed asymmetry patterns that were consistent between different connection weighting approaches. Connection weighting based on fractional anisotropy and neurite density produced the most consistent results. Our proposed method also showed considerable agreement with an alternative technique based on connectome averaging. The new computational method and results of this study can be useful for assessing normal and abnormal development of the structural connectome early in life.

Building pondscapes for amphibian metapopulations.

The success of ponds constructed to restore ecological infrastructure for pond-breeding amphibians and benefit aquatic biodiversity depends on where and how they are built. We studied effects of pond and landscape characteristics, including connectivity, on metapopulation dynamics of 12 amphibian species in Switzerland. To understand the determinants of long-term occupancy (here summarized as incidence), environmental effects on both colonization and persistence should be considered. We fitted dynamic occupancy models to 20years of monitoring data on a pond construction program to quantify effects of pond and landscape characteristics and different connectivity metrics on colonization and persistence probabilities in constructed ponds. Connectivity to existing populations explained dynamics better than structural connectivity metrics, and simple metrics (distance to the nearest neighbor population, population density) were useful surrogates for dispersal kernel-weighted metrics commonly used in metapopulation theory. Population connectivity mediated the persistence of conservation target species in new ponds, suggesting source-sink dynamics in newly established populations. Population density captured this effect well and could be used by practitioners for site selection. Ponds created where there were 2-4 occupied ponds within a radius of ∼0.5km had >3.5 times higher incidence of target species (median) than isolated ponds. Species had individual preferences regarding pond characteristics, but breeding sites with larger (≥100m2) total water surface area, that temporarily dried, and that were in surroundings with maximally 50% forest benefitted multiple target species. Pond diversity will foster amphibian diversity at the landscape scale.

Percentage of area protected can substitute for more complicated structural metrics when monitoring protected area connectivity

Protected area (PA) targets have become a hallmark of global conservation policies such as the recent Kunming-Montreal Global Biodiversity Framework, which requires signatory countries to establish new PAs while also monitoring gains to both PA network coverage and connectivity at large spatial scales. Policy makers tasked with implementing and monitoring progress toward these targets face the difficult decision about which connectivity metric to use, which is not always straightforward given existing data and software limitations. We empirically compare 17 of the most widely used structural connectivity metrics to determine how they capture change in a PA network as additional protected areas are added and assess whether mathematically simple metrics are a reasonable substitute for more complex metrics. We find that simply reporting the percentage of the total area that is protected is a viable way to capture connectivity gains in most landscapes. If a more involved metric is desired, we recommend the Integral Index of Connectivity, which was highly correlated with the percentage of area protected, produced similar results when measuring change in PA networks, and incorporates stepping stone movements through transboundary regions.

Lconnect R package: A versatile tool for evaluating landscape connectivity and prioritizing habitat patches in conservation research

Biodiversity conservation faces significant challenges due to human-induced climate and habitat change. Understanding these threats’ effects and interactions is crucial for effective conservation planning. Here, we present the lconnect R package, a user-friendly tool for assessing landscape connectivity and prioritizing habitat patches. The package allows researchers to analyze and quantify structural connectivity metrics and prioritize patches based on their contribution to overall landscape connectivity. By facilitating the evaluation of landscape connectivity and patch prioritization, lconnect supports informed decision-making in landscape ecology and conservation biology. The package's advantages, relative to other available tools, are discussed, and an example of its application is presented. Overall, the lconnect R package presents a valuable addition to the toolbox of landscape ecologists, empowering them to better understand and address the challenges of habitat fragmentation and loss in biodiversity conservation.

Bridging structural and functional hydrological connectivity in dryland ecosystems

On dryland hillslopes, vegetation water availability is often subsidized by the redistribution of rainfall runoff from bare soil (sources) to vegetation patches (sinks). In regions where rainfall volumes are too low to support spatially continuous plant growth, such functional connectivity between bare soil and vegetated areas enables the establishment and persistence of dryland ecosystems. Increasing the connectivity within bare soil areas can intensify runoff and increase water losses from hillslopes, disrupting this redistribution and reducing the water available to sustain ecosystem function. Inferring functional connectivity (from bare to vegetated, or within bare areas) from structural landscape features is an attractive approach to enable rapid, scalable characterization of dryland ecosystem function from remote observations. Such inference, however, would rely on metrics of structural connectivity, which describe the contiguity of bare soil areas. Several studies have observed non-stationarity in the relations between functional and structural connectivity metrics as rainfall conditions vary. Consequently, the suitability of using structural connectivity to provide a reliable proxy for functional connectivity remains uncertain and motivates the work here.Relations between structural and functional connectivity metrics are established based on model simulations of rainfall-runoff on hillslopes with varying soil properties and vegetation patterns. These relations vary between two hydrologic limits – a ‘local’ (patch-scale) limit, in which functional connectivity is related to structural connectivity, and a ‘global’ (hillslope-scale) limit, in which functional connectivity is most related to the hillslope vegetation fraction regardless of the structural connectivity of bare soil areas. The transition between these limits within the simulations depends on rainfall intensity and duration, and soil permeability. While the local limit may strengthen positive feedbacks between vegetation and water availability, the implications of these limits for dryland functioning need further exploration, particularly considering the timescale separation between storm runoff production and vegetation growth.

513 Presurgical Identification of Seizure Onset and Propagative Zones Without Ictal Recordings: A Combined Diffusion MRI and SEEG Study

INTRODUCTION: Increasing evidence has suggested that identification of regions involved in early seizure propagation (“Propagation Zones”, PZ) is important to predict seizure freedom after epilepsy surgery (Andrews 2019). Resting-state connectivity analyses using stereo-electroencephalography (SEEG) have shown promise in efficiently characterizing seizure onset zones (SOZ) but have found difficulty in distinguishing both SOZs and PZs from non-involved brain regions. Recently, evidence has suggested that ictal structure-function coupling can be used to delineate brain regions important in seizure dynamics (Shah 2019). METHODS: We calculated the resting-state SEEG directed connectivity of 26 consented patients with focal epilepsy undergoing presurgical evaluation. Using preoperative diffusion MRI, we then implemented a custom technique to obtain structural connectivity metrics between SEEG contacts. We calculated the structural connectivity and structure-function coupling of SOZs, PZs (spread within 10 seconds), and non-involved regions over a range of Euclidean distances. Finally, we generated models using a support vector machine to classify SOZs, PZs, and non-involved regions. RESULTS: SOZs and PZs exhibit comparably high local structural connectivity compared to non-involved regions despite SOZs demonstrating significantly greater functional connectivity to both PZs and non-involved regions. However, PZs exhibit significantly higher local structure-function coupling to that of non-involved regions, with SOZs exhibiting the highest local structure-function coupling. A support vector machine to classify SOZs, PZs, and non-involved regions and was able to significantly increase model accuracy by incorporating local structure-function coupling. CONCLUSIONS: SOZs and PZs demonstrate a distinct local structure-function coupling to that of non-involved regions and each other. This distinct coupling profile can be used to accurately classify SOZs, PZs and non-involved regions.

Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference.

Characterizing a particular neurodegenerative condition against others possible diseases remains a challenge along clinical, biomarker, and neuroscientific levels. This is the particular case of frontotemporal dementia (FTD) variants, where their specific characterization requires high levels of expertise and multidisciplinary teams to subtly distinguish among similar physiopathological processes. Here, we used a computational approach of multimodal brain networks to address simultaneous multiclass classification of 298 subjects (one group against all others), including five FTD variants: behavioral variant FTD, corticobasal syndrome, nonfluent variant primary progressive aphasia, progressive supranuclear palsy, and semantic variant primary progressive aphasia, with healthy controls. Fourteen machine learning classifiers were trained with functional and structural connectivity metrics calculated through different methods. Due to the large number of variables, dimensionality was reduced, employing statistical comparisons and progressive elimination to assess feature stability under nested cross-validation. The machine learning performance was measured through the area under the receiver operating characteristic curves, reaching 0.81 on average, with a standard deviation of 0.09. Furthermore, the contributions of demographic and cognitive data were also assessed via multifeatured classifiers. An accurate simultaneous multiclass classification of each FTD variant against other variants and controls was obtained based on the selection of an optimum set of features. The classifiers incorporating the brain's network and cognitive assessment increased performance metrics. Multimodal classifiers evidenced specific variants' compromise, across modalities and methods through feature importance analysis. If replicated and validated, this approach may help to support clinical decision tools aimed to detect specific affectations in the context of overlapping diseases.

Brain white matter extracellular free-water increases are related to reduced neurocognitive function in systemic lupus erythematosus.

Brain white matter (WM) microstructural changes evaluated by diffusion MRI are well documented in patients with SLE. Yet, the conventional diffusion tensor imaging technique fails to differentiate WM changes that originate from tissue alterations from those due to increased extracellular free water (FW) related to neuroinflammation, microvascular disruption, atrophy, or other extracellular processes. Here, we sought to delineate changes in WM tissue microstructure and extracellular FW volume and examine their relationships with neurocognitive function in SLE patients. Twenty SLE patients [16 females, aged 36.0 (10.6)] without clinically overt neuropsychiatric manifestation and 61 healthy controls (HCs) [29 females, aged 29.2 (9.4)] underwent diffusion MRI and computerized neuropsychological assessments cross-sectionally. The FW imaging method was applied to compare microstructural tissue changes and extracellular FW volume of the brain WM between SLE patients and HCs. Association between extracellular FW changes and neurocognitive performance was studied. SLE patients had higher WM extracellular FW compared with HCs (family-wise-error-corrected P < 0.05), while no group difference was found in FW-corrected tissue compartment and structural connectivity metrics. Extracellular FW increases in SLE patients were associated with poorer neurocognitive performance that probed sustained attention (P = 0.022) and higher cumulative glucocorticoid dose (P = 0.0041). Such findings remained robust after controlling for age, gender, intelligence quotient, and total WM volume. The association between WM extracellular FW increases and reduced neurocognitive performance suggest possible microvascular degradation and/or neuroinflammation in SLE patients with clinically inactive disease. The mechanistic impact of cumulative glucocorticoids on WM FW deserves further evaluation.

Investigating the brain structural connectome following working memory training in children born extremely preterm or extremely low birth weight.

Children born extremely preterm (EP, <28weeks' gestation) or extremely low birth weight (ELBW, <1,000g) are a vulnerable population at high risk of working memory impairments. We aimed to examine changes in the brain structural connectivity networks thought to underlie working memory performance, after completion of a working memory training program (Cogmed) compared with a placebo program in EP/ELBW children. This was a double-blind, placebo-controlled randomized trial (the Improving Memory in a Preterm Randomised Intervention Trial). Children born EP/ELBW received either the Cogmed or placebo program at 7years of age (n=91). A subset of children had magnetic resonance imaging of the brain immediately pre- and 2weeks post-training (Cogmed n=28; placebo n=27). T1 -weighted and diffusion-weighted images were used to perform graph theoretical analysis of structural connectivity networks. Changes from pre-training to post-training in structural connectivity metrics were generally similar between randomized groups. There was little evidence that changes in structural connectivity metrics were related to changes in working memory performance from pre- to post-training. Overall, our results provide little evidence that the Cogmed working memory training program has training-specific effects on structural connectivity networks in EP/ELBW children.

Joint cortical surface and structural connectivity analysis of Alzheimer's Disease.

Prior neuroimaging studies have demonstrated isolated structural and connectivity changes in the brain due to Alzheimer's Disease (AD). However, how these changes relate to each other is not well understood. We present a preliminary study to begin to fill this gap by leveraging joint independent component analysis (jICA). We explore how jICA performs in an analysis of T1 and diffusion weighted MRI by characterizing the joint changes of complex cortical surface and structural connectivity metrics in AD in subjects from the Baltimore Longitudinal Study of Aging. We calculate 588 region-based cortical metrics and 4,753 fractional anisotropy-based connectivity metrics and project them into a low-dimensional manifold with principal component analysis. We perform jICA on the manifold and subsequently backproject the independent components to the original data space. We demonstrate component stability with 3-fold cross validation and find differential component loadings between 776 cognitively unimpaired control subjects and 23 with AD that generalizes across folds. In addition, we perform the same analysis on the surface and connectivity metrics separately and find that the joint approach identifies both novel and similar components to the separate approaches. To illustrate the joint approach's primary utility, we provide an example hypothesis for how surface and connectivity components may vary together with AD. These preliminary results suggest jointly varying independent cortical surface and structural connectivity components can be consistently extracted from MRI data and provide a data-driven way for generating novel hypotheses about AD that may not be captured by separate analyses.

Joint analysis of structural connectivity and cortical surface features: correlates with mild traumatic brain injury.

Mild traumatic brain injury (mTBI) is a complex syndrome that affects up to 600 per 100,000 individuals, with a particular concentration among military personnel. About half of all mTBI patients experience a diverse array of chronic symptoms which persist long after the acute injury. Hence, there is an urgent need for better understanding of the white matter and gray matter pathologies associated with mTBI to map which specific brain systems are impacted and identify courses of intervention. Previous works have linked mTBI to disruptions in white matter pathways and cortical surface abnormalities. Herein, we examine these hypothesized links in an exploratory study of joint structural connectivity and cortical surface changes associated with mTBI and its chronic symptoms. Briefly, we consider a cohort of 12 mTBI and 26 control subjects. A set of 588 cortical surface metrics and 4,753 structural connectivity metrics were extracted from cortical surface regions and diffusion weighted magnetic resonance imaging in each subject. Principal component analysis (PCA) was used to reduce the dimensionality of each metric set. We then applied independent component analysis (ICA) both to each PCA space individually and together in a joint ICA approach. We identified a stable independent component across the connectivity-only and joint ICAs which presented significant group differences in subject loadings (p<0.05, corrected). Additionally, we found that two mTBI symptoms, slowed thinking and forgetfulness, were significantly correlated (p<0.05, corrected) with mTBI subject loadings in a surface-only ICA. These surface-only loadings captured an increase in bilateral cortical thickness.

Accounting for Changing Structure in Functional Network Analysis of TBI Patients.

Over the last 15 years, network analysis approaches based on MR data have allowed a renewed understanding of the relationship between brain function architecture and consciousness. Application of this approach to Disorders of Consciousness (DOC) highlights the relationship between specific aspects of network topology and levels of consciousness. Nonetheless, such applications do not acknowledge that DOC patients present with a dramatic level of heterogeneity in structural connectivity (SC) across groups (e.g., etiology, diagnostic categories) and within individual patients (e.g., over time), which possibly affects the level and quality of functional connectivity (FC) patterns that can be expressed. In addition, it is rarely acknowledged that the most frequently employed outcome metrics in the study of brain connectivity (e.g., degree distribution, inter- or intra-resting state network connectivity, and clustering coefficient) are interrelated and cannot be assumed to be independent of each other. We present empirical data showing that, when the two points above are not taken into consideration with an appropriate analytic model, it can lead to a misinterpretation of the role of each outcome metric in the graph's structure and thus misinterpretation of FC results. We show that failing to account for either SC or the inter-relation between outcome measures can lead to inflated false positives (FP) and/or false negatives (FN) in inter- or intra-resting state network connectivity results (defined, respectively, as a positive or negative result in network connectivity that is present when not accounting for SC and/or outcome measure inter-relation, but becomes not significant when accounting for all variables). Overall, we find that unconscious patients have lower rates of FP and FN for within cortical connectivity, lower rates of FN for cortico-subcortical connectivity, and lower rates of FP for within subcortical connectivity. These lower rates in unconscious patients may reflect differences in their triadic closure and SC metrics, which bias the interpretations of the inter- or intra-resting state network connectivity if the SC metrics and triadic closure are not modeled. We suggest that future studies of functional connectivity in DOC patients (i) incorporate where possible SC metrics and (ii) properly account for the intercorrelated nature of outcome variables.

Age-related change in episodic memory: role of functional and structural connectivity between the ventral posterior cingulate and the parietal cortex.

While the neural correlates of age-related episodic memory decline have been extensively studied, the precise involvement of the Posterior Cingulate Cortex (PCC) and posterior parietal cortex (the precuneus and the angular gyrus), remains unclear. The present study examined functional and structural neural correlates of age-related episodic memory change assessed over 12years in 120 older adults (range 76-90years). Episodic memory performance was measured using the Free and Cued Selective Reminding Test (FCSRT); functional connectivity metrics were computed from resting-state fMRI images and structural connectivity metrics were assessed through microstructural properties of reconstructed tract using a native space pipeline. We found that FCSRT change was significantly associated with the functional connectivity between the ventral PCC and three parietal regions, the ventral superior, the inferior part of the precuneus, and the rostro dorsal part of the angular gyrus. This association was independent of hippocampal volume. In addition, we found the that change in FCSRT scores was associated with fractional anisotropy of the tract connecting the ventral PCC and the ventral superior part of the precuneus. Change in episodic memory in aging was therefore related to a combination of high functional connectivity and low structural connectivity between the ventral PCC and the ventral superior part of the precuneus.

Effects of cropland encroachment on prairie pothole wetlands: numbers, density, size, shape, and structural connectivity

Agricultural expansion is the greatest source of wetland loss in the Prairie Pothole Region (PPR) of North and South Dakota, a critical waterfowl production area in North America. It is unknown how wetland losses from grassland conversion may alter structural connectivity in the prairie pothole network, however. We examined how agricultural expansion over the period 2001–2011 altered the number, size, shape, and structural connectivity of PPR wetlands. We hypothesized that the loss of wetlands or wetland area would decrease structural connectivity on the landscape. We analyzed a published raster database that quantified 2001–2011 agricultural conversion of wetlands in the Dakotas. A suite of structural connectivity metrics was computed using the igraph R package. Wetland area decreased by 25% within the study area, density decreased by 16%, and average size decreased from 2.41 to 2.16 ha with no increase in perimeter:area ratios, thus indicating changes more from the splitting of larger wetlands (accounting for 23% of area lost) and “nibbling” at patch area (38%) than from complete wetland elimination (39%). Despite loss of wetlands and wetland area to cropland, however, the network did not display constrained structural connectivity. Structural connectivity has not been significantly affected by wetland losses because of the large number of remaining wetlands, but wetlands will continue to be lost with ongoing grassland conversion and climate shifts. It is unknown where the tipping point of wetland losses lies in the PPR that will incur ecological costs.

Changes in structural network are associated with cortical demyelination in early multiple sclerosis.

The aim of this study was to investigate the interplay between structural connectivity and cortical demyelination in early multiple sclerosis. About 27 multiple sclerosis patients and 18 age-matched controls underwent two MRI scanning sessions. The first was done at 7T and involved acquiring quantitative T1 and T2 * high-resolution maps to estimate cortical myelination. The second was done on a Connectom scanner and consisted of acquiring high angular resolution diffusion-weighted images to compute white matter structural connectivity metrics: strength, clustering and local efficiency. To further investigate the interplay between structural connectivity and cortical demyelination, patients were divided into four groups according to disease-duration: 0-1 year, 1-2 years, 2-3 years, and >3 years. ANOVA and Spearman's correlations were used to highlight relations between metrics. ANOVA detected a significant effect between disease duration and both cortical myelin (p = 2 × 10-8 ) and connectivity metrics (p < 10-4 ). We observed significant cortical myelin loss in the shorter disease-duration cohorts (0-1 year, p = .0015), and an increase in connectivity in the longer disease-duration cohort (2-3 years, strength: p = .01, local efficiency: p = .002, clustering: p = .001). Moreover, significant covariations between myelin estimation and white matter connectivity metrics were observed: Spearman's Rho correlation coefficients of 0.52 (p = .0003), 0.55 (p = .0001), and 0.53 (p = .0001) for strength, local efficiency, and clustering, respectively. An association between cortical myelin loss and changes in white matter connectivity in early multiple sclerosis was detected. These changes in network organization might be the result of compensatory mechanisms in response to the ongoing cortical diffuse damage in the early stages of multiple sclerosis.

Graph theory as an invasive species management tool: case study in the Sonoran Desert

Biodiversity in arid regions is usually concentrated around limited water resources, so natural resource managers have constructed artificial water catchments in many areas to supplement natural waters. Because invasive species may also use these waters, dispersing into previously inaccessible areas, the costs and benefits of artificial waters must be gauged and potential invasion- and climate change-management strategies assayed. We present a network analysis framework to identify waters that likely contribute to the spread of invasive species. Using the Sonoran Desert waters network and the American bullfrog (Lithobates catesbeianus)—a known predator, competitor, and carrier of pathogens deadly to other amphibians—as an example, we quantified the structural connectivity of the network to predict regional invasion potential under current and two future scenarios (climate change and management reduction) to identify waters to manage and monitor for invasive species. We identified important and vulnerable waters based on connectivity metrics under scenarios representing current conditions, projected climate-limited conditions, and conditions based on removal of artificial waters. We identified 122,607 km2 of land that could be used as a buffer against invasion and 67,745 km2 of land that could be augmented by artificial water placement without facilitating invasive species spread. Structural connectivity metrics can be used to evaluate alternative management strategies for invasive species and climate mitigation.

Structural connectivity differences in motor network between tremor-dominant and nontremor Parkinson's disease

Motor phenotypes of Parkinson's disease (PD) are recognized to have different prognosis and therapeutic response, but the neural basis for this clinical heterogeneity remains largely unknown. The main aim of this study was to compare differences in structural connectivity metrics of the main motor network between tremor-dominant and nontremor PD phenotypes (TD-PD and NT-PD, respectively) using probabilistic tractography-based network analysis. A total of 63 PD patients (35 TD-PD patients and 28 NT-PD patients) and 30 healthy controls underwent a 3T MRI. Next, probabilistic tractography-based network analysis was performed to assess structural connectivity in cerebello-thalamo-basal ganglia-cortical circuits, by measuring the connectivity indices of each tract and the efficiency of each node. Furthermore, dopamine transporter single-photon emission computed tomography (DAT-SPECT) with 123 I-ioflupane was used to assess dopaminergic striatal depletion in all PD patients. Both PD phenotypes showed nodal abnormalities in the substantia nigra, in agreement with DAT-SPECT evaluation. In addition, NT-PD patients displayed connectivity alterations in nigro-pallidal and fronto-striatal pathways, compared with both controls and TD-PD patients, in which the same motor connections seemed to be relatively spared. Of note, in NT-PD group, rigidity-bradykinesia score correlated with fronto-striatal connectivity abnormalities. These findings demonstrate that structural connectivity alterations occur in the cortico-basal ganglia circuit of NT-PD patients, but not in TD-PD patients, suggesting that these anatomical differences may underlie different motor phenotypes of PD. Hum Brain Mapp 38:4716-4729, 2017. © 2017 Wiley Periodicals, Inc.

Análisis del cambio en la conectividad estructural del paisaje (1975-2008) de la cuenca del lago Cuitzeo, Michoacán, México

The Cuitzeo Lake Basin is covered with forests and scrublands with high ecological importance, which have experienced fragmentation resulting from human activities. In this study we analyzed changes in the structural connectivity of the Cuitzeo Lake basin, Michoacan, between 1975 and 2008. We used land cover databases for the years 1975, 1996, 2000, 2003 and 2008. For each year, landscape composition metrics were applied, such as isolation and structural connectivity metrics at the landscape and class levels. Finally we performed a comparative analysis. The forest cover presented low structural connectivity values in contrast to the human activity related land covers, which dominated the landscape over time. The information on changes in landscape structural connectivity and fragmentation from this study provide a useful tool for landscape management and biodiversity conservation.

Abstract 129: Predicting Patients Who Will Have Cognitive Decline After Carotid Endarterectomy or Stenting Using Structural Connectivity Graph Analysis

Objectives: Many CEA and CAS patients experience postoperative neurocognitive decline. We sought to apply structural connectivity metrics to identify patients at increased risk for postoperative decline based solely on preoperative imaging. Methods: Under an IRB approved protocol 28 patients underwent presurgical T1 structural and 30 direction diffusion tensor imaging (DTI) MRI and neuropsychological tests before and 1 month after surgery. Patients with decline showed decreased performance on the Rey-AVLT on 1 month follow up. The T1 images were processed using FreeSurfer 5.3, with resulting segmentations reviewed and edited as needed under neuroradiologist supervision. Whole brain tractography was performed using Diffusion Toolkit and visually inspected. Connectivity matrices were then generated, and graph metrics were computed using the Brain Connectivity Toolbox. Results: Controlling for age, classifiers using the graph analysis metrics “weighted optimal community structure” &amp; “binary component sizes” were able to identify patients that would experience cognitive decline with 81% sensitivity 83% and specificity (p&lt;.05, false discovery rate .05). These two measures were computed at 10 proportion edge thresholds from .1 to 1 at intervals of .1 in weighted and binary networks respectively. Conclusions: Applying preoperative structural connectivity analysis in CEA and CAS patients may identify patients at increased risk for postoperative cognitive decline, and in so doing may help risk stratify patients and guide them to preventive interventions.

Landscape connectivity and animal behavior: functional grain as a key determinant for dispersal

Landscape connectivity can be viewed from two perspectives that could be considered as extremes of a gradient: functional connectivity (refers to how the behavior of a dispersing organism is affected by landscape structure and elements) and structural connectivity (depends on the spatial configuration of habitat patches in the landscape like vicinity or presence of barriers). Here we argue that dispersal behavior changes with landscape configuration stressing the evolutionary dimension that has often been ignored in landscape ecology. Our working hypothesis is that the functional grain of resource patches in the landscape is a crucial factor shaping individual movements, and therefore influencing landscape connectivity. Such changes are likely to occur on the short-term (some generations). We review empirical studies comparing dispersal behavior in landscapes differing in their fragmentation level, i.e., with variable resource grain. We show that behavioral variation affecting each of the three stages of the dispersal process (emigration, displacement or transfer in the matrix, and immigration) is indeed likely to occur according to selective pressures resulting from changes in the grain of the landscape (mortality or deferred costs). Accordingly, landscape connectivity results from the interaction between the dispersal behavior of individuals and the grain of each particular landscape. The existence of this interaction requires that connectivity estimates (being based on individual-based models, least cost distance algorithms, and structural connectivity metrics or even Euclidian distance) should be carefully evaluated for their applicability with respect to the required level of precision in species-specific and landscape information.