Coastal blue carbon ecosystems, typically comprising interconnected habitat mosaics, are globally important pathways of carbon sequestration and play a significant role in climate change regulation and mitigation. Current coastal management strategies often rely on simplified regional carbon stock estimates, that overlook the geographical variability and intricate ecological dynamics within these ecosystems. This study adopts a seascape ecology approach to evaluate the role of multiple seascape characteristics on carbon storage in two arid region coastal lagoons. We show that seascape location is the most influential driver of carbon stocks. Additionally, carbon isotopic variability, a proxy for connectivity, can be as influential as habitat type, particularly in the UAQ lagoon. This challenges the conventional reliance on data from individual habitat types (e.g., seagrass, mangrove, or tidal marsh) and highlights the context-dependency of carbon stocks. Moreover, the specific characteristics driving carbon stocks vary between seascapes: in Khor Faridah, connectivity to seagrass and mangrove habitats is crucial, while in the UAQ lagoon, sheltered and elevated areas are more influential. Our findings suggest that the interconnectivity between different habitat types, such as mangroves and saltmarshes, significantly enhances carbon storage. This is especially pronounced in large, sheltered mangrove habitat types within upper intertidal zones. Notably, small patches of mangroves, up to 10 ha, are associated with an approximate 10 % increase in carbon stocks. These results underscore the need for a more holistic, context-specific approach to designing nature-based solutions for coastal management and ecosystem restoration. By considering the specific characteristics and connectivity of seascape mosaics, we can more effectively enhance carbon stock potential in coastal ecosystems. This study contributes to a deeper spatially explicit understanding of the complex factors influencing carbon stocks in blue carbon ecosystems, highlighting the importance of tailored management strategies that reflect the unique ecological patterns of each seascape.

UNASSIGNED: An existing model suggests that some brain features of relatives of people affected by psychosis can be distinguished from both the probands and a control group.  Such findings can be interpreted as representing a compensating mechanism.
UNASSIGNED: We studied white matter features using diffusion tensor imaging in a cohort of 82 people affected by psychosis, 122 of their first-degree relatives, and 89 control subjects that were scanned between two to three times with an interval of approximately 3 years between consecutive scans. We measured both fractional anisotropy and other standard diffusivity measures such as axial diffusivity. Additionally, we calculated standard connectivity measures such as path length based on probabilistic or deterministic tractography. Finally, by averaging the values of the different measures over the two or three consecutive scans, we studied epoch-averagely the difference between these three groups.
UNASSIGNED: For several tracts and several connectivity measures, the relatives showed distinct features from both the probands and the control groups. In those cases, the relatives did not necessarily score between the probands and the control group. An aggregate analysis in the form of a group-dependent score for the different modes of the analysis (e.g., for fractional anisotropy) supported this observation.
UNASSIGNED: We interpret these results as evidence supporting a compensation mechanism in the brain of relatives that may be related to resilience that some of them exhibit in the face of the genetic risk they have for being affected by psychosis.

Evidence accumulates that the cerebellum\'s role in the brain is not restricted to motor functions. Rather, cerebellar activity seems to be crucial for a variety of tasks that rely on precise event timing and prediction. Due to its complex structure and importance in communication, human speech requires a particularly precise and predictive coordination of neural processes to be successfully comprehended. Recent studies proposed that the cerebellum is indeed a major contributor to speech processing, but how this contribution is achieved mechanistically remains poorly understood. The current study aimed to reveal a mechanism underlying cortico-cerebellar coordination and demonstrate its speech-specificity. In a reanalysis of magnetoencephalography data, we found that activity in the cerebellum aligned to rhythmic sequences of noise-vocoded speech, irrespective of its intelligibility. We then tested whether these \"entrained\" responses persist, and how they interact with other brain regions, when a rhythmic stimulus stopped and temporal predictions had to be updated. We found that only intelligible speech produced sustained rhythmic responses in the cerebellum. During this \"entrainment echo,\" but not during rhythmic speech itself, cerebellar activity was coupled with that in the left inferior frontal gyrus, and specifically at rates corresponding to the preceding stimulus rhythm. This finding represents evidence for specific cerebellum-driven temporal predictions in speech processing and their relay to cortical regions.

The brain connectivity-based atlas is a promising tool for understanding neural communication pathways in the brain, gaining relevance in predicting personalized outcomes for various brain pathologies. This critical review examines the robustness of the brain connectivity-based atlas for predicting post-stroke outcomes. A comprehensive literature search was conducted from 2012 to May 2023 across PubMed, Scopus, EMBASE, EBSCOhost, and Medline databases. Twenty-one studies were screened, and through analysis of these studies, we identified 18 brain connectivity atlases employed by the studies for lesion analysis in their predictions. The brain atlases were assessed for study cohorts, connectivity measures, identified brain regions, atlas applications, and limitations. Based on the analysis of these studies, most atlases were based on diffusion tensor imaging and resting-state functional magnetic resonance imaging (MRI). Studies predicting post-stroke functional outcomes relied on the atlases for multivariate lesion analysis and region of interest identification, often employing atlases derived from young, healthy populations. Current brain connectivity-based atlases for stroke applications lack standardized methods to define and map brain connectivity across atlases and cover sensorimotor functional connectivity to a limited extent. In conclusion, this review highlights the need to develop more comprehensive, robust, and adaptable brain connectivity-based atlases specifically tailored to post-stroke populations.

Objective biomarkers have been a critical challenge for the field of psychiatry, where diagnostic, prognostic, and theranostic assessments are still based on subjective narratives. Psychopathology operates with idiographic knowledge and subjective evaluations incorporated into clinical assessment inventories, but is considered to be a medical discipline and, as such, uses medical intervention methods (e.g., pharmacological, ECT; rTMS; tDCS) and, therefore, is supposed to operate with the language and methods of nomothetic networks. The idiographic assessments are provisionally \"quantified\" into \"structured clinical scales\" to in some way resemble nomothetic measures. Instead of fostering data merging and integration, this approach further encapsulates the clinical psychiatric methods, as all other biological tests (molecular, neuroimaging) are performed separately, only after the clinical assessment has provided diagnosis. Translational cross-validation of clinical assessment instruments and fMRI is an attempt to address the gap. The aim of this approach is to investigate whether there exist common and specific neural circuits, which underpin differential item responses to clinical self-rating scales during fMRI sessions in patients suffering from the two main spectra of mental disorders: schizophrenia and major depression. The current status of this research program and future implications to promote the development of psychiatry as a medical discipline are discussed.

The neural network mediating successful response inhibition mainly includes right hemisphere activation of the pre-supplementary motor area, inferior frontal gyrus (IFG), subthalamic nucleus (STN), and caudate nucleus. However, the causal role of these regions in the inhibitory network is undefined. Five patients with Parkinson\'s disease were assessed prior to and after therapeutic thermal ablation of the right STN in two separate functional magnetic resonance imaging (fMRI) sessions while performing a stop-signal task. Initiation times were faster but motor inhibition with the left hand (contralateral to the lesion) was significantly impaired as evident in prolonged stop-signal reaction times. Reduced inhibition after right subthalamotomy was associated (during successful inhibition) with the recruitment of basal ganglia regions outside the established inhibitory network. They included the putamen and caudate together with the anterior cingulate cortex and IFG of the left hemisphere. Subsequent network connectivity analysis (with the seed over the nonlesioned left STN) revealed a new inhibitory network after right subthalamotomies. Our results highlight the causal role of the right STN in the neural network for motor inhibition and the possible basal ganglia mechanisms for compensation upon losing a key node of the inhibition network.

Winter flounder Pseudopleuronectes americanus (Walbaum 1792) are a coastal flatfish species of economic and cultural importance that have dwindled to <15, % of their historic abundance in the southern New England/Mid-Atlantic region of the United States, with evidence indicating near-extirpation of certain local populations. This species exhibits intricate behaviors in spawning and migration that contribute to population complexity and resilience. These behaviors encompass full or partial philopatry to natal estuaries, the generation of multiple pulses of larval delivery, and partial migration. The patterns of genetic diversity within and among estuaries and cohorts presented here carry important implications in understanding the susceptibility to demographic shocks, even if the full extent of genetic diversity within and among winter flounder stocks on the US East Coast remains unresolved. Our findings reveal connectivity between estuaries in Long Island, New York, suggesting the potential for genetic rescue of depleted subpopulations. Family reconstruction and relatedness analysis indicate that split cohorts and migration contingents are not the result of genetically distinct lineages. We found no evidence for genetic structure separating these groups, and in some instances, we were able to detect closely related individuals that belonged to different migratory contingents or cohorts. Characterizing the spatial and behavioral organization of this species at the population level is crucial for comprehending its potential for recovery, not only in terms of biomass but also in reinstating the complex population structure that supports resilience. The search for generality in winter flounder spawning and migration behavior remains elusive, but perhaps the lack of generalities within this species is what has allowed it to persist in the face of decades of environmental and anthropogenic stressors.

Dispersal is a complex series of movements before an individual establishes a home range. Animals must travel and forage in unfamiliar landscapes that include anthropogenic risks such as road crossings, harvest, and urban landscapes. We compare dispersal behavior of juvenile mountain lions (Puma concolor) from two geographically distinct populations in California and Nevada, USA. These two sites are ecologically similar but have different management practices; hunting is permitted in Nevada, whereas mountain lions are protected in California. We used GPS-collar data and net-squared displacement analysis to identify three dispersal states: exploratory, departure, and transient home range. We then compared each dispersal state of the two mountain lion populations using an integrated step selection analysis (iSSA). The model included explanatory variables hypothesized to influence one or more dispersal states, including distance to forest, shrub, water, hay and crop, developed lands, and four-wheel drive roads, as well as elevation and terrain ruggedness. Results revealed consistent habitat selection between sites across most landscape variables, with one notable exception: anthropogenic covariates, including distance to developed land, distance to hay and crop, and distance to four-wheeled drive roads, were only statistically significant on modeled habitat selection during dispersal in the population subject to hunting (i.e., Nevada). Results suggest that hunting (pursuit with hounds resulting in harvest) and non-lethal pursuit (pursuit with hounds but no harvest allowed) increase avoidance of anthropogenic landscapes during dispersal for juvenile mountain lions. By comparing populations, we provided valuable insights into the role of management in shaping dispersal behavior.

Stroke is a severe medical condition which may lead to permanent disability conditions. The initial 8 weeks following a stroke are crucial for rehabilitation, as most recovery occurs during this period. Personalized approaches and predictive biomarkers are needed for tailored rehabilitation. In this context, EEG brain connectivity and Artificial Intelligence (AI) can play a crucial role in diagnosing and predicting stroke outcomes efficiently. In the present study, 127 patients with subacute ischemic lesions and 90 age- and gender-matched healthy controls were enrolled. EEG recordings were obtained from each participant within 15 days of stroke onset. Clinical evaluations were performed at baseline and at 40-days follow-up using the National Institutes of Health Stroke Scale (NIHSS). Functional connectivity analysis was conducted using Total Coherence (TotCoh) and Small Word (SW). Quadratic support vector machines (SVM) algorithms were implemented to classify healthy subjects compared to stroke patients (Healthy vs Stroke), determine the affected hemisphere (Left vs Right Hemisphere), and predict functional recovery (Functional Recovery Prediction). In the classification for Functional Recovery Prediction, an accuracy of 94.75%, sensitivity of 96.27% specificity of 92.33%, and AUC of 0.95 were achieved; for Healthy vs Stroke, an accuracy of 99.09%, sensitivity of 100%, specificity of 98.46%, and AUC of 0.99 were achieved. For Left vs Right Hemisphere classification, accuracy was 86.77%, sensitivity was 91.44%, specificity was 80.33%, and AUC was 0.87. These findings highlight the potential of utilizing functional connectivity measures based on EEG in combination with AI algorithms to improve patient outcomes by targeted rehabilitation interventions.

Theta burst stimulation (TBS) is a non-invasive brain stimulation technique that can modulate neural activity. The effect of TBS on regions beyond the motor cortex remains unclear. With increased interest in applying TBS to non-motor regions for research and clinical purposes, these effects must be understood and characterised. We synthesised the electrophysiological effects of a single session of TBS, as indexed by electroencephalography (EEG) and concurrent transcranial magnetic stimulation and EEG (TMS-EEG), in non-clinical participants. We reviewed 79 studies that administered either continuous TBS (cTBS) or intermittent TBS (iTBS) protocols. Broadly, cTBS suppressed and iTBS facilitated evoked response component amplitudes. Response to TBS as measured by spectral power and connectivity was much more variable. Variability increased in the presence of task stimuli. There was a large degree of heterogeneity in the research methodology across studies. Additionally, the effect of individual differences on TBS response is insufficiently investigated. Future research investigating the effects of TBS as measured by EEG must consider methodological and individual factors that may affect TBS outcomes.