SNAPSHOT USA 2021: A third coordinated national camera trap survey of the United States.

SNAPSHOT USA is a multicontributor, long-term camera trap survey designed to survey mammals across the United States. Participants are recruited through community networks and directly through a website application (https://www.snapshot-usa.org/). The growing Snapshot dataset is useful, for example, for tracking wildlife population responses to land use, land cover, and climate changes across spatial and temporal scales. Here we present the SNAPSHOT USA 2021 dataset, the third national camera trap survey across the US. Data were collected across 109 camera trap arrays and included 1711 camera sites. The total effort equaled 71,519 camera trap nights and resulted in 172,507 sequences of animal observations. Sampling effort varied among camera trap arrays, with a minimum of 126 camera trap nights, a maximum of 3355 nights, a median 546 nights, and a mean 656 ± 431 nights. This third dataset comprises 51 camera trap arrays that were surveyed during 2019, 2020, and 2021, along with 71 camera trap arrays that were surveyed in 2020 and 2021. All raw data and accompanying metadata are stored on Wildlife Insights (https://www.wildlifeinsights.org/), and are publicly available upon acceptance of the data papers. SNAPSHOT USA aims to sample multiple ecoregions in the United States with adequate representation of each ecoregion according to its relative size. Currently, the relative density of camera trap arrays varies by an order of magnitude for the various ecoregions (0.22-5.9 arrays per 100,000 km2), emphasizing the need to increase sampling effort by further recruiting and retaining contributors. There are no copyright restrictions on these data. We request that authors cite this paper when using these data, or a subset of these data, for publication. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the US Government.

Degeneration in Nucleus basalis of Meynert signals earliest stage of Alzheimer's disease progression.

Nucleus Basalis of Meynert (NbM), a crucial source of cholinergic projection to the entorhinal cortex (EC) and hippocampus (HC), has shown sensitivity to neurofibrillary degeneration in the early stages of Alzheimer's Disease. Using deformation-based morphometry (DBM) on up-sampled MRI scans from 1447 Alzheimer's Disease Neuroimaging Initiative participants, we aimed to quantify NbM degeneration along the disease trajectory. Results from cross-sectional analysis revealed significant differences of NbM volume between cognitively normal and early mild cognitive impairment cohorts, confirming recent studies suggesting that NbM degeneration happens before degeneration in the EC or HC. Longitudinal linear mixed-effect models were then used to compare trajectories of volume change after realigning all participants into a common timeline based on their cognitive decline. Results indicated the earliest deviations in NbM volumes from the cognitively healthy trajectory, challenging the prevailing idea that Alzheimer's originates in the EC. Converging evidence from cross-sectional and longitudinal models suggest that the NbM may be a focal target of early AD progression, which is often obscured by normal age-related decline.

Atypical co-development of the thalamus and cortex in autism: Evidence from age-related white-gray contrast change.

Recent studies have shown that white-gray contrast (WGC) of either cortical or subcortical gray matter provides for accurate predictions of age in typically developing (TD) children, and that, at least for the cortex, it changes differently with age in subjects with autism spectrum disorder (ASD) compared to their TD peers. Our previous study showed different patterns of contrast change between ASD and TD in sensorimotor and association cortices. While that study was confined to the cortex, we hypothesized that subcortical structures, particularly the thalamus, were involved in the observed cortical dichotomy between lower and higher processing. The current paper investigates that hypothesis using the WGC measures from the thalamus in addition to those from the cortex. We compared age-related WGC changes in the thalamus to those in the cortex. To capture the simultaneity of this change across the two structures, we devised a metric capturing the co-development of the thalamus and cortex (CoDevTC), proportional to the magnitude of cortical and thalamic age-related WGC change. We calculated this metric for each of the subjects in a large homogeneous sample taken from the Autism Brain Imaging Data Exchange (ABIDE) (N = 434). We used structural MRI data from the largest high-quality cross-sectional sample (NYU) as well as two other large high-quality sites, GU and OHSU, all three using Siemens 3T scanners. We observed that the co-development features in ASD and TD exhibit contrasting patterns; specifically, some higher-order thalamic nuclei, such as the lateral dorsal nucleus, exhibited reduction in codevelopment with most of the cortex in ASD compared to TD. Moreover, this difference in the CoDevTC pattern correlates with a number of behavioral measures across multiple cognitive and physiological domains. The results support previous notions of altered connectivity in autism, but add more specific evidence about the heterogeneity in thalamocortical development that elucidates the mechanisms underlying the clinical features of ASD.

VentRa: distinguishing frontotemporal dementia from psychiatric disorders.

The volume of the lateral ventricles is a reliable and sensitive indicator of brain atrophy and disease progression in behavioural variant frontotemporal dementia. In this study, we validate our previously developed automated tool using ventricular features (known as VentRa) for the classification of behavioural variant frontotemporal dementia versus a mixed cohort of neurodegenerative, vascular and psychiatric disorders from a clinically representative independent dataset. Lateral ventricles were segmented for 1110 subjects-14 behavioural variant frontotemporal dementia, 30 other frontotemporal dementia, 70 Lewy body disease, 898 Alzheimer's disease, 62 vascular brain injury and 36 primary psychiatric disorder from the publicly accessible National Alzheimer's Coordinating Center dataset to assess the performance of VentRa. Using ventricular features to discriminate behavioural variant frontotemporal dementia subjects from primary psychiatric disorders, VentRa achieved an accuracy rate of 84%, a sensitivity rate of 71% and a specificity rate of 89%. VentRa was able to identify behavioural variant frontotemporal dementia from a mixed age-matched cohort (i.e. other frontotemporal dementia, Lewy body disease, Alzheimer's disease, vascular brain injury and primary psychiatric disorders) and to correctly classify other disorders as 'not compatible with behavioral variant frontotemporal dementia' with a specificity rate of 83%. The specificity rates against each of the other individual cohorts were 80% for other frontotemporal dementia, 83% for Lewy body disease, 83% for Alzheimer's disease, 84% for vascular brain injury and 89% for primary psychiatric disorders. VentRa is a robust and generalizable tool with potential usefulness for improving the diagnostic certainty of behavioural variant frontotemporal dementia, particularly for the differential diagnosis with primary psychiatric disorders.

Associations between neuromelanin depletion and cortical rhythmic activity in Parkinson's disease.

Background and Objectives: Parkinson's disease (PD) is marked by the death of neuromelanin-rich dopaminergic and noradrenergic cells in the substantia nigra (SN) and the locus coeruleus (LC), respectively, resulting in motor and cognitive impairments. While SN dopamine dysfunction has clear neurophysiological effects, the impact of reduced LC norepinephrine signaling on brain activity in PD remains to be established. Methods: We used neuromelanin-sensitive T1-weighted MRI (NPD = 58; NHC = 27) and task-free magnetoencephalography (NPD = 58; NHC = 65) to identify neuropathophysiological factors related to the degeneration of the LC and SN in patients with PD. Results: We found pathological increases in rhythmic alpha (8 - 12 Hz) activity in patients with decreased LC neuromelanin, with a stronger association in patients with worse attentional impairments. This negative alpha-LC neuromelanin relationship is also stronger in fronto-motor cortices, which are regions with high densities of norepinephrine transporters in the healthy brain, and where alpha activity is negatively related to attention scores. These observations support a noradrenergic association between LC integrity and alpha band activity. Our data also show that rhythmic beta (15 - 29 Hz) activity in the left somato-motor cortex decreases with lower levels of SN neuromelanin; the same regions where beta activity reflects axial motor symptoms. Discussion: Together, our findings clarify the association of well-documented alterations of rhythmic neurophysiology in PD with cortical and subcortical neurochemical systems. Specifically, attention-related alpha activity reflects dysfunction of the noradrenergic system, and beta activity with relevance to motor impairments reflects dopaminergic dysfunction.

Continuous Instrument Tracking in a Cerebral Corticectomy Ex Vivo Calf Brain Simulation Model: Face and Content Validation.

BACKGROUND AND OBJECTIVES: Subpial corticectomy involving complete lesion resection while preserving pial membranes and avoiding injury to adjacent normal tissues is an essential bimanual task necessary for neurosurgical trainees to master. We sought to develop an ex vivo calf brain corticectomy simulation model with continuous assessment of surgical instrument movement during the simulation. A case series study of skilled participants was performed to assess face and content validity to gain insights into the utility of this training platform, along with determining if skilled and less skilled participants had statistical differences in validity assessment. METHODS: An ex vivo calf brain simulation model was developed in which trainees performed a subpial corticectomy of three defined areas. A case series study assessed face and content validity of the model using 7-point Likert scale questionnaires. RESULTS: Twelve skilled and 11 less skilled participants were included in this investigation. Overall median scores of 6.0 (range 4.0-6.0) for face validity and 6.0 (range 3.5-7.0) for content validity were determined on the 7-point Likert scale, with no statistical differences between skilled and less skilled groups identified. CONCLUSION: A novel ex vivo calf brain simulator was developed to replicate the subpial resection procedure and demonstrated face and content validity.

Differences in Alzheimer's Disease-Related Pathology Profiles Across Apolipoprotein Groups.

The apolipoprotein (APOE) ɛ4 allele is a risk factor for Alzheimer's disease (AD), whereas the ɛ2 allele is thought to be protective against AD. Few studies have examined the relationship between brain pathologies, atrophy, white matter hyperintensities (WMHs) and APOE status in those with the ɛ2ɛ4 genotype and results are inconsistent for those with an ɛ2 allele. Alzheimer's disease neuroimaging participants were divided into 1 of 4 APOE allele profiles (E4 = ɛ4ɛ4 or ɛ3ɛ4; E2 = ɛ2ɛ2 or ɛ2ɛ3; E3 = ɛ3ɛ3; or E24 = ɛ2ɛ4). Linear mixed models examined the relationship between APOE profiles and brain changes (i.e., regional WMHs, ventricle size, hippocampal and entorhinal cortex volume, amyloid level, and phosphorylated tau measures), while controlling for age, sex, education, and diagnostic status at baseline and over time. APOE ɛ4 was associated with increased pathology, whereas ɛ2 positivity is associated with reduced baseline and lower accumulation of pathologies and neurodegeneration. APOE ɛ2ɛ4 was similar to ɛ4 (increased neurodegeneration) but with a slower rate of change. The strong associations observed between APOE and pathology show the importance of how genetic factors influence structural brain changes. These findings suggest that ɛ2ɛ4 genotype is related to increased declines associated with the ɛ4 as opposed to the protective effects of the ɛ2. These findings have important implications for initiating treatments and interventions. Given that people with the ɛ2ɛ4 genotype can expect to have increased atrophy, they should be considered (alongside those with an ɛ4) in targeted interventions to reduce brain changes that occur with AD.

Genome sequences of cluster GA phages Phonegingi and Dropshot.

Bacteriophages Phonegingi and Dropshot were isolated from soil in North Carolina using the host Microbacterium foliorum. Both phages have siphovirus morphologies. Based on gene content similarity to one another and to other actinobacteriophages, both phages are assigned to phage cluster GA.

The virulence of Dichelobacter nodosus, measured by the elastase test, is an important predictor for virulent footrot diagnosis in New South Wales sheep flocks.

Ovine footrot is a contagious bacterial disease that causes foot lesions, and depending on the virulence of the causative strains, may lead to severe underrunning of the hoof and lameness. Virulent footrot can be identified, treated and controlled more effectively than less virulent benign forms. The in vitro elastase test for virulence of the causative bacteria, Dichelobacter nodosus, has been used to support clinical diagnosis. However, not all laboratory-designated virulent D. nodosus strains cause clinical signs of virulent footrot. This study evaluated retrospectively how well the elastase test supported clinical footrot diagnosis in 150 sheep flocks examined for suspect footrot in New South Wales between August 2020 and December 2021. Flocks were included if measures of clinical disease, environmental conditions and the virulence of D. nodosus isolates were available. Variation in the elastase activity result between D. nodosus isolated from the same flock made bacterial virulence hard to interpret, but calculating the mean elastase rate for all isolates from the same flock made correlations between bacterial virulence and flock footrot diagnosis possible. Simplifying bacterial virulence into whether there were any elastase-positive D. nodosus isolates before 12 days increased the predictive value of elastase results for virulent diagnosis, compared with using the first day that any isolate was elastase positive or the percentage of elastase-positive isolates by 12 days, but not all clinically virulent flocks had isolates with elastase activity before 12 days. Logistic regression models were fitted to identify the minimum number of predictors for virulent footrot diagnosis, with models suggesting that virulent footrot diagnosis was best predicted by adding the elastase test result and environmental conditions to the prevalence of severe foot lesions (score 4 and 5). However, performing the same analysis with different breeds, ages of sheep and seasons might highlight other factors important in the diagnosis of virulent footrot.

Is predictive coding falsifiable?

Predictive-coding has justifiably become a highly influential theory in Neuroscience. However, the possibility of its unfalsifiability has been raised. We argue that if predictive-coding were unfalsifiable, it would be a problem, but there are patterns of behavioural and neuroimaging data that would stand against predictive-coding. Contra (vanilla) predictive patterns are those in which the more expected stimulus generates the largest evoked-response. However, basic formulations of predictive-coding mandate that an expected stimulus should generate little, if any, prediction error and thus little, if any, evoked-response. It has, though, been argued that contra (vanilla) predictive patterns can be obtained if precision is higher for expected stimuli. Certainly, using precision, one can increase the amplitude of an evoked-response, turning a predictive into a contra (vanilla) predictive pattern. We demonstrate that, while this is true, it does not present an absolute barrier to falsification. This is because increasing precision also reduces latency and increases the frequency of the response. These properties can be used to determine whether precision-weighting in predictive-coding justifiably explains a contra (vanilla) predictive pattern, ensuring that predictive-coding is falsifiable.

A state-of-the-art review on deep learning for estimating eloquent cortex from resting-state fMRI.

Deep learning algorithms have greatly improved our ability to estimate eloquent cortex regions from resting-state brain scans for patients about to undergo neurosurgery. The use of deep learning has the potential to fully automate functional mapping of cortex in this context. We present a highly focused state-of-the-art review on current technology for estimating eloquent cortex from resting-state functional magnetic resonance scans and identify potential paths to meet this goal in the future.

Bloody noise: The impact of blood-flow artifacts on registration.

Blood-flow artifacts present a serious challenge for most, if not all, volumetric analytical approaches. We utilize T1-weighted data with prominent blood-flow artifacts from the Autism Brain Imaging Data Exchange (ABIDE) multisite agglomerative dataset to assess the impact that such blood-flow artifacts have on registration of T1-weighted data to a template. We use a heuristic approach to identify the blood-flow artifacts in these data; we use the resulting blood masks to turn the underlying voxels to the intensity of the cerebro-spinal fluid, thus mimicking the effect of blood suppression. We then register both the original data and the deblooded data to a common T1-weighted template, and compare the quality of those registrations to the template in terms of similarity to the template. The registrations to the template based on the deblooded data yield significantly higher similarity values compared with those based on the original data. Additionally, we measure the nonlinear deformations needed to transform the data from the position achieved by registering the original data to the template to the position achieved by registering the deblooded data to the template. The results indicate that blood-flow artifacts may seriously impact data processing that depends on registration to a template, that is, most all data processing.

Longitudinal changes in hippocampal texture from healthy aging to Alzheimer's disease.

Early detection of Alzheimer's disease is essential to develop preventive treatment strategies. Detectible change in brain volume emerges relatively late in the pathogenic progression of disease, but microstructural changes caused by early neuropathology may cause subtle changes in the MR signal, quantifiable using texture analysis. Texture analysis quantifies spatial patterns in an image, such as smoothness, randomness and heterogeneity. We investigated whether the MRI texture of the hippocampus, an early site of Alzheimer's disease pathology, is sensitive to changes in brain microstructure before the onset of cognitive impairment. We also explored the longitudinal trajectories of hippocampal texture across the Alzheimer's continuum in relation to hippocampal volume and other biomarkers. Finally, we assessed the ability of texture to predict future cognitive decline, over and above hippocampal volume. Data were acquired from the Alzheimer's Disease Neuroimaging Initiative. Texture was calculated for bilateral hippocampi on 3T T1-weighted MRI scans. Two hundred and ninety-three texture features were reduced to five principal components that described 88% of total variance within cognitively unimpaired participants. We assessed cross-sectional differences in these texture components and hippocampal volume between four diagnostic groups: cognitively unimpaired amyloid-ß- (n = 406); cognitively unimpaired amyloid-ß+ (n = 213); mild cognitive impairment amyloid-ß+ (n = 347); and Alzheimer's disease dementia amyloid-ß+ (n = 202). To assess longitudinal texture change across the Alzheimer's continuum, we used a multivariate mixed-effects spline model to calculate a 'disease time' for all timepoints based on amyloid PET and cognitive scores. This was used as a scale on which to compare the trajectories of biomarkers, including volume and texture of the hippocampus. The trajectories were modelled in a subset of the data: cognitively unimpaired amyloid-ß- (n = 345); cognitively unimpaired amyloid-ß+ (n = 173); mild cognitive impairment amyloid-ß+ (n = 301); and Alzheimer's disease dementia amyloid-ß+ (n = 161). We identified a difference in texture component 4 at the earliest stage of Alzheimer's disease, between cognitively unimpaired amyloid-ß- and cognitively unimpaired amyloid-ß+ older adults (Cohen's d = 0.23, Padj = 0.014). Differences in additional texture components and hippocampal volume emerged later in the disease continuum alongside the onset of cognitive impairment (d = 0.30-1.22, Padj < 0.002). Longitudinal modelling of the texture trajectories revealed that, while most elements of texture developed over the course of the disease, noise reduced sensitivity for tracking individual textural change over time. Critically, however, texture provided additional information than was provided by volume alone to more accurately predict future cognitive change (d = 0.32-0.63, Padj < 0.0001). Our results support the use of texture as a measure of brain health, sensitive to Alzheimer's disease pathology, at a time when therapeutic intervention may be most effective.

Hippocampal grading provides higher classification accuracy for those in the AD trajectory than hippocampal volume.

Much research has focused on neurodegeneration in aging and Alzheimer's disease (AD). We developed Scoring by Nonlocal Image Patch Estimator (SNIPE), a non-local patch-based measure of anatomical similarity and hippocampal segmentation to measure hippocampal change. While SNIPE shows enhanced predictive power over hippocampal volume, it is unknown whether SNIPE is more strongly associated with group differences between normal controls (NC), early MCI (eMCI), late (lMCI), and AD than hippocampal volume. Alzheimer's Disease Neuroimaging Initiative older adults were included in the first analyses (N = 1666, 513 NCs, 269 eMCI, 556 lMCI, and 328 AD). Sub-analyses investigated amyloid positive individuals (N = 834; 179 NC, 148 eMCI, 298 lMCI, and 209 AD) to determine accuracy in those on the AD trajectory. We compared SNIPE grading, SNIPE volume, and Freesurfer volume as features in seven different machine learning techniques classifying participants into their correct cohort using 10-fold cross-validation. The best model was then validated in the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL). SNIPE grading provided the highest classification accuracy for all classifications in both the full and amyloid positive sample. When classifying NC:AD, SNIPE grading provided an 89% accuracy (full sample) and 87% (amyloid positive sample). Freesurfer volume provided much lower accuracies of 65% (full sample) and 46% (amyloid positive sample). In the AIBL validation cohort, SNIPE grading provided a 90% classification accuracy for NC:AD. These findings suggest SNIPE grading provides increased classification accuracy over both SNIPE and Freesurfer volume. SNIPE grading offers promise to accurately identify people with and without AD.

Differences in AD-related pathology profiles across APOE groups.

BACKGROUND: The apolipoprotein (APOE) e4 allele is a known risk factor for Alzheimer's disease (AD), while the e2 allele is thought to be protective against AD. Few studies have examined the relationship between brain pathologies, atrophy, and white matter hyperintensities (WMHs) and APOE status in those with the e2e4 genotype and results are inconsistent for those with an e2 allele. METHODS: We analyzed Alzheimer's Disease Neuroimaging participants that had APOE genotyping and at least one of the following metrics: regional WMH load, ventricle size, hippocampal (HC) and entorhinal cortex (EC) volume, amyloid level (i.e., AV-45), and phosphorylated tau (pTau). Participants were divided into one of four APOE allele profiles (E4=e4e4 or e3e4; E2=e2e2 or e2e3; E3=e3e3; or E24=e2e4, Fig.1). Linear mixed models examined the relationship between APOE profiles and each pathology (i.e., regional WMHs, ventricle size, hippocampal and entorhinal cortex volume, amyloid level, and phosphorylated tau measures). while controlling for age, sex, education, and diagnostic status at baseline and over time. RESULTS: APOE ε4 is associated with increased pathology while ε2 positivity is associated with reduced baseline and lower accumulation of pathologies and rates of neurodegeneration. APOE ε2ε4 is similar to ε4 (increased neurodegeneration) but with a slower rate of change. CONCLUSIONS: The strong associations observed between APOE and pathology in this study show the importance of how genetic factors influence structural brain changes. These findings suggest that ε2ε4 genotype is related to increased declines associated with the ε4 as opposed to the protective effects of the ε2. These findings have important implications for initiating treatments and interventions. Given that people who have the ε2ε4 genotype can expect to have increased atrophy, they must be included (alongside those with an ε4 profile) in targeted interventions to reduce brain changes that occur with AD.

Big data from small animals: integrating multi-level environmental data into the Dog Aging Project.

Environmental exposures can have large impacts on health outcomes. While many resources have been dedicated to understanding how humans are influenced by the environment, few efforts have been made to study the role of built and natural environmental features on animal health. The Dog Aging Project (DAP) is a longitudinal community science study of aging in companion dogs. Using a combination of owner-reported surveys and secondary sources linked through geocoded coordinates, DAP has captured home, yard and neighbourhood variables for over 40,000 dogs. The DAP environmental data set spans four domains: the physical and built environment; chemical environment and exposures; diet and exercise; and social environment and interactions. By combining biometric data, measures of cognitive function and behaviour, and medical records, DAP is attempting to use a big-data approach to transform the understanding of how the surrounding world affects the health of companion dogs. In this paper, the authors describe the data infrastructure developed to integrate and analyse multi-level environmental data that can be used to improve the understanding of canine co-morbidity and aging.

L'impact des expositions environnementales sur la santé est parfois considérable. Si diverses ressources ont été consacrées à décrire l'influence de l'environnement sur les humains, les efforts visant à étudier l'effet des paramètres environnementaux, tant naturels qu'anthropiques, sur la santé animale sont plus rares. Le Dog Aging Project (DAP) est une étude scientifique longitudinale à base communautaire portant sur le vieillissement du chien de compagnie. À partir d'observations notifiées par les propriétaires de chiens et de sources secondaires reliées par des coordonnées de géocodage, le DAP a réuni des variables sur le foyer d'habitation, l'environnement extérieur immédiat et le voisinage de plus de 40 000 chiens. Les séries de données environnementales du DAP couvrent quatre domaines : l'environnement physique et bâti ; l'environnement chimique et les expositions ; le régime alimentaire et la dépense physique ; et les interactions et l'environnement social. En combinant les données biométriques, les mesures du fonctionnement cognitif et comportemental et les dossiers médicaux, le DAP cherche à utiliser l'approche des mégadonnées pour transformer notre perception de la manière dont le monde qui nous entoure affecte la santé des chiens de compagnie. Les auteurs décrivent l'infrastructure des données mise au point pour intégrer et analyser des données environnementales multi-niveaux, afin de mieux comprendre les phénomènes de comorbidité et de vieillissement chez le chien.

La exposición a factores ambientales puede tener muchas e importantes repercusiones en los resultados sanitarios. Si bien se han dedicado cuantiosos recursos a aprehender la influencia del entorno en las personas, poco se ha hecho para estudiar el modo en que las características del medio, tanto natural como artificial, repercuten en la salud de los animales. El proyecto sobre Envejecimiento canino [Dog Aging Project: DAP] es un estudio longitudinal de ciencia ciudadana centrado en el envejecimiento de los perros de compañía. Combinando la información de encuestas realizadas a propietarios y de fuentes secundarias y vinculando los datos a coordenadas geográficas codificadas, el DAP ha permitido reunir información de variables ligadas al hogar, el jardín y el barrio de más de 40 000 perros. El conjunto de datos ambientales del DAP cubre cuatro grandes ámbitos: medio físico y urbanizado; condiciones químicas del entorno y exposición a sustancias químicas; régimen alimentario y ejercicio; y medio e interacciones sociales. Pasando por el uso combinado de datos biométricos, historias clínicas y mediciones de la función cognitiva y el comportamiento, el DAP apunta ahora a emplear técnicas de trabajo con macrodatos para hacer evolucionar nuestras ideas sobre la influencia del mundo que nos rodea en la salud de los perros de compañía. Los autores describen la infraestructura de datos establecida para integrar y analizar datos ambientales multiestratificados que nos ayuden a conocer mejor los procesos de comorbilidad y envejecimiento en el perro.

SEEGAtlas: A framework for the identification and classification of depth electrodes using clinical images.

Objective.Accurate localization, classification, and visualization of intracranial electrodes are fundamental for analyzing intracranial electrographic recordings. While manual contact localization is the most common approach, it is time-consuming, prone to errors, and is particularly challenging and subjective in low quality images, which are common in clinical practice. Automatically locating and interactively visualizing where each of the 100-200 individual contacts records in the brain is essential for understanding the neural origins of intracranial EEG.Approach.We introduced the SEEGAtlas plugin for the IBIS system, an open-source software platform for image-guided neurosurgery and multi-modal image visualization. SEEGAtlas extends IBIS functionalities to semi-automatically locate depth-electrode contact coordinates and automatically label the tissue type and anatomical region in which each contact is located. To illustrate the capabilities of SEEGAtlas and to validate the algorithms, clinical magnetic resonance images (MRIs) before and after electrode implantation of ten patients with depth electrodes implanted to localize the origin of their epileptic seizures were analyzed.Main Results. Visually identified contact coordinates were compared with the coordinates obtained by SEEGAtlas, resulting in a median difference of 1.4 mm. The agreement was lower for MRIs with weak susceptibility artifacts than for high-quality images. The tissue type was classified with 86% agreement with visual inspection. The anatomical region was classified as having a median agreement across patients of 82%.Significance. The SEEGAtlas plugin is user-friendly and enables accurate localization and anatomical labeling of individual contacts along implanted electrodes, together with powerful visualization tools. Employing the open-source SEEGAtlas results in accurate analysis of the recorded intracranial electroencephalography (EEG), even when only suboptimal clinical imaging is available. A better understanding of the cortical origin of intracranial EEG would help improve clinical interpretation and answer fundamental questions of human neuroscience.

Deviation From Normative Whole Brain and Deep Gray Matter Growth in Children With MOGAD, MS, and Monophasic Seronegative Demyelination.

BACKGROUND AND OBJECTIVES: Pediatric-acquired demyelination of the CNS associated with antibodies directed against myelin oligodendrocyte glycoprotein (MOG; MOG antibody-associated disease [MOGAD]) occurs as a monophasic or relapsing disease and with variable but often extensive T2 lesions in the brain. The impact of MOGAD on brain growth during maturation is unknown. We quantified the effect of pediatric MOGAD on brain growth trajectories and compared this with the growth trajectories of age-matched and sex-matched healthy children and children with multiple sclerosis (MS, a chronic relapsing disease known to lead to failure of normal brain growth and to loss of brain volume) and monophasic seronegative demyelination. METHODS: We included children enrolled at incident attack in the prospective longitudinal Canadian Pediatric Demyelinating Disease Study who were recruited at the 3 largest enrollment sites, underwent research brain MRI scans, and were tested for serum MOG-IgG. Children seropositive for MOG-IgG were diagnosed with MOGAD. MS was diagnosed per the 2017 McDonald criteria. Monophasic seronegative demyelination was confirmed in children with no clinical or MRI evidence of recurrent demyelination and negative results for MOG-IgG and aquaporin-4-IgG. Whole and regional brain volumes were computed through symmetric nonlinear registration to templates. We computed age-normalized and sex-normalized z scores for brain volume using a normative dataset of 813 brain MRI scans obtained from typically developing children and used mixed-effect models to assess potential deviation from brain growth trajectories. RESULTS: We assessed brain volumes of 46 children with MOGAD, 26 with MS, and 51 with monophasic seronegative demyelinating syndrome. Children with MOGAD exhibited delayed (p < 0.001) age-expected and sex-expected growth of thalamus, caudate, and globus pallidus, normalized for the whole brain volume. Divergence from expected growth was particularly pronounced in the first year postonset and was detected even in children with monophasic MOGAD. Thalamic volume abnormalities were less pronounced in children with MOGAD compared with those in children with MS. DISCUSSION: The onset of MOGAD during childhood adversely affects the expected trajectory of growth of deep gray matter structures, with accelerated changes in the months after an acute attack. Further studies are required to better determine the relative impact of monophasic vs relapsing MOGAD and whether relapsing MOGAD with attacks isolated to the optic nerves or spinal cord affects brain volume over time.

A dataset of multi-contrast unbiased average MRI templates of a Parkinson's disease population.

Parkinson's disease (PD) is a complex neurodegenerative disorder affecting regions such as the substantia nigra (SN), red nucleus (RN) and locus coeruleus (LC). Processing MRI data from patients with PD requires anatomical structural references for spatial normalization and structural segmentation. Extending our previous work, we present multi-contrast unbiased MRI templates using nine 3T MRI modalities: T1w, T2*w, T1-T2* fusion, R2*, T2w, PDw, fluid-attenuated inversion recovery (FLAIR), susceptibility-weighted imaging, and neuromelanin-sensitive MRI (NM). One mm isotropic voxel size templates were created, along with 0.5 mm isotropic whole brain templates and 0.3 mm isotropic templates of the midbrain. All templates were created from 126 PD patients (44 female; ages=40-87), and 17 healthy controls (13 female; ages=39-84), except the NM template, which was created from 85 PD patients and 13 controls, respectively. The dataset is available on the NIST MNI Repository via the following link: http://nist.mni.mcgill.ca/multi-contrast-pd126-and-ctrl17-templates/. The data is also available on NITRC at the following link: https://www.nitrc.org/projects/pd126/.

Association of cumulative prenatal adversity with infant subcortical structure volumes and child problem behavior and its moderation by a coexpression polygenic risk score of the serotonin system.

Prenatal adversity has been linked to later psychopathology. Yet, research on cumulative prenatal adversity, as well as its interaction with offspring genotype, on brain and behavioral development is scarce. With this study, we aimed to address this gap. In Finnish mother-infant dyads, we investigated the association of a cumulative prenatal adversity sum score (PRE-AS) with (a) child emotional and behavioral problems assessed with the Strengths and Difficulties Questionnaire at 4 and 5 years (N = 1568, 45.3% female), (b) infant amygdalar and hippocampal volumes (subsample N = 122), and (c) its moderation by a hippocampal-specific coexpression polygenic risk score based on the serotonin transporter (SLC6A4) gene. We found that higher PRE-AS was linked to greater child emotional and behavioral problems at both time points, with partly stronger associations in boys than in girls. Higher PRE-AS was associated with larger bilateral infant amygdalar volumes in girls compared to boys, while no associations were found for hippocampal volumes. Further, hyperactivity/inattention in 4-year-old girls was related to both genotype and PRE-AS, the latter partially mediated by right amygdalar volumes as preliminary evidence suggests. Our study is the first to demonstrate a dose-dependent sexually dimorphic relationship between cumulative prenatal adversity and infant amygdalar volumes.