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Biodiversity is a multifaceted concept that often eludes simple operational definitions. As a result, a variety of definitions have been proposed each with varying levels of complexity and scope. While different definitions of biodiversity exist, the basic unit of measurement for the vast majority of studies is conducted at the species level. Traditional approaches to measuring species richness provide useful, yet spatially constrained information. Remote sensing offers the opportunity for large area characterizations of biodiversity in a systematic, repeatable, and spatially exhaustive manner. Based on this review we examine the potential for a national biodiversity monitoring system for Canada driven by remote sensing, a country approaching 1 billion ha in area, with the aim of producing recommendations that are transferable for regional or continental applications. A combination of direct and indirect approaches is proposed, with four selected key indicators of diversity that can be derived from Earth observation data: productivity, disturbance, topography, and land cover. Monitoring these indicators through time at an ecosystem level has the potential to provide a national early warning system, indicating where areas of potential biodiversity change may be occurring. We believe the large area biodiversity monitoring system as outlined would provide an initial stratification of key areas where regional and local scale analysis can be focused, while also providing context-specific information for species collection data.

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Background and Purpose: Motor deficit is the most common disability after stroke, and early prediction of motor outcome is critical for early interventions. Here, we constructed a fine map of the corticospinal tract (CST) for early prediction of motor outcome and for understanding the secondary brain changes after subcortical stroke. Methods: Diffusion spectrum imaging data from 50 healthy adults were used to reconstruct fine maps of CST with different origins, including primary motor area (M1), primary sensory area (S1), premotor cortex, and supplementary motor area (SMA). Their diffusion properties correlated with motor functions in healthy adults. The impacts of the impairments of different CST on motor outcomes and on structural and functional changes of brain were investigated in 136 patients with subcortical stroke by combining CST damage-symptom association study and voxel-based lesion-symptom mapping. Results: In healthy adults, the isotropy of M1 fiber correlated with walking endurance and that of SMA fiber with motor dexterity. In chronic stroke patients, the integrity of M1 and SMA fibers showed the most significant correlation with motor deficits. The percentage of early damage of M1 and SMA fibers correlated with that of chronic motor deficits. Voxel-based lesion-symptom mapping revealed that acute stroke lesions in the bilateral M1 and right SMA fibers were associated with chronic motor deficits. The early damage of M1 fiber negatively correlated with the integrity of M1-M1 fiber, and the early damage of SMA fiber negatively correlated with gray matter volume of the contralateral cerebellum in the chronic stage. Conclusions: The CST that originated from the M1 and SMA are closely associated with motor outcomes and brain structural changes, and the fine maps of CST from these 2 cortical areas are useful in assessing and predicting long-term motor outcome in patients with subcortical stroke.

Alzheimer's disease (AD) is associated with disruptions in brain activity and networks. However, there is substantial inconsistency among studies that have investigated functional brain alterations in AD; such contradictions have hindered efforts to elucidate the core disease mechanisms. In this study, we aim to comprehensively characterize AD‐associated functional brain alterations using one of the world's largest resting‐state functional MRI (fMRI) biobank for the disorder. The biobank includes fMRI data from six neuroimaging centers, with a total of 252 AD patients, 221 mild cognitive impairment (MCI) patients and 215 healthy comparison individuals. Meta‐analytic techniques were used to unveil reliable differences in brain function among the three groups. Relative to the healthy comparison group, AD was associated with significantly reduced functional connectivity and local activity in the default‐mode network, basal ganglia and cingulate gyrus, along with increased connectivity or local activity in the prefrontal lobe and hippocampus ( p < .05, Bonferroni corrected). Moreover, these functional alterations were significantly correlated with the degree of cognitive impairment (AD and MCI groups) and amyloid‐β burden. Machine learning models were trained to recognize key fMRI features to predict individual diagnostic status and clinical score. Leave‐one‐site‐out cross‐validation established that diagnostic status (mean area under the receiver operating characteristic curve: 0.85) and clinical score (mean correlation coefficient between predicted and actual Mini‐Mental State Examination scores: 0.56, p < .0001) could be predicted with high accuracy. Collectively, our findings highlight the potential for a reproducible and generalizable functional brain imaging biomarker to aid the early diagnosis of AD and track its progression.