Homogeneous Regions Research Articles

Rich north, poor south - Regionalization of European water retention: The landscape hydric potential concept

Natural water retention varies greatly across Europe. Understanding the key ecosystem factors that affect the quantity, quality, availability, and spatiotemporal distribution of water resources is essential for effective landscape management. This study presents the Landscape Hydric Potential (LHP) index of Europe, which serves as an indicator of the natural environment's ability to infiltrate and retain water. The LHP index was aggregated into 12 relatively homogeneous regions, each representing similar values of natural water retention. The main factors influencing the hydric potential, and thus the water resources of Europe, include climatic conditions, geomorphology, soil properties, and land cover and use, especially the proportion of urbanized areas. Northern Europe, in comparison to southern Europe, exhibits higher LHP index values, indicating greater retention capacities. The climatic water balance is the main factor determine poorer retention capacities in the southern part of Europe. In northern Europe positive values of climatic water balance and slope inclination promote to higher LHP value and thus higher retention capacity. The results also demonstrate that there are sufficient, freely available, and precise data to assess water retention potential - landscape hydric potential - even at a continental scale.

Chronic Δ9-tetrahydrocannabinol exposure in adolescent nonhuman primates: persistent abnormalities in economic demand and brain functional connectivity.

Although chronic cannabis use during adolescence can alter brain function and impair complex behavioral processes, it is unclear whether such deficits persist into adulthood. Using a coordinated awake neuroimaging and behavioral approach in nonhuman primates, we addressed this issue by examining the impact of chronic adolescent exposure to Δ9-tetrahydrocannabinol (THC) on brain functional connectivity and motivational processes during early adulthood. Female and male squirrel monkeys (n = 23) were treated daily for 6 months during adolescence with vehicle or either a low (0.32 mg/kg) or high dose (3.2 mg/kg) of THC. Regional homogeneity and seed-to-whole-brain functional connectivity were analyzed prior to, during, and following discontinuation of chronic treatment to examine changes in regions implicated in reward processing. Subsequently, motivation and reward sensitivity in these subjects, now young adults, were evaluated in economic demand studies by determining the relationship between escalating response requirements and consumption of differing magnitudes of a palatable food reinforcer. Results show that adolescent THC exposure led to persistent alterations in mOFC, caudate, and ventral striatum whole-brain connectivity. Moreover, subjects treated with vehicle during adolescence displayed an orderly and expected inverse relationship between reward magnitude and demand elasticity, whereas THC-treated subjects exhibited dosage-dependent disorder in reward sensitivity and motivational deficits. Changes in neural circuitry (local connectivity in ventral striatum and whole brain connectivity in mOFC) and economic demand were correlated with indices of reward sensitivity in vehicle- but not THC-treated subjects. Taken together, these data indicate that chronic adolescent THC exposure produced long-lasting neurocognitive abnormalities in reward processing.

Abnormal changes of brain function and structure in patients with T2DM-related cognitive impairment: a neuroimaging meta-analysis and an independent validation.

Type 2 diabetes mellitus (T2DM) seriously threatens human health and the quality of life, cognitive impairment is considered as a common complication of T2DM. Neuroimaging meta-analysis found brain functional and structural abnormality in patients with T2DM. Therefore, the purpose of the meta-analysis was to identify brain regions of patients with T2DM-related cognitive impairment (T2DM-CI) where functional and structural indicators changed together or could not synchronize. A literature screening of neuroimaging studies on cognitive impairment in T2DM was conducted from 1 January 2007 to 26 May 2023 in PubMed, Web of Science, Cochrane Library, and Medline databases. The functional indicators we studied were amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo) and degree centrality (DC), while the structural indicator was gray matter (GM), which included gray matter volume (GMV) and cerebral cortical thickness. Studies reporting ALFF, ReHo, DC and GM abnormalities between T2DM-CI and healthy controls (HCs) were selected and their significant peak coordinates (x, y, z) and effect size (t-value) were extracted to perform a meta-analysis using anisotropic effect size sign differential mapping (AES-SDM) 5.15 software. Moreover, the brain regions with significant differences obtained from meta-analysis were saved as masks and then validated in our data. Total 19 studies and 20 datasets were involved in this study. Compared to HCs, combining ALFF, ReHo, and DC measurements, the brain activity of the left anterior cingulate/paracingulate gyri (ACC.L, BA24) in T2DM-CI patients increased significantly, while the brain activity of the left lingual gyrus (LING.L, BA18) in T2DM-CI patients decreased significantly. The GM indicator of the right superior temporal gyrus (STG.R, BA42) and left inferior occipital gyrus (IOG.L, BA19) in T2DM-CI patients decreased significantly. Meta-regression analysis showed the negative relationship between the brain activity reduction in LING.L and the percentage of female patients, as well as the negative relationship between GM reduction in IOG.L and T2DM duration. Furthermore, we validated a decrease in brain activity in the LING.L of T2DM-CI patients in our independent dataset. The decrease of brain activity in LING.L and the decrease of GM in IOG.L were closely related to visual impairment in T2DM-CI patients. These abnormal brain regions may be the main targets for future research, early intervention can delay the further development of cognitive impairment in T2DM patients and improve their quality of life, which also provided early biomarkers for clarifying the mechanism of cognitive impairment in T2DM.

Diagnostics and Prognostics with High Dimensional Spatial-Temporal Data: From Structures to Human Brains

Diagnostics and prognostics with high-dimensional spatial-temporal data require innovative methodologies due to the inherent complexity of such datasets. This thesis explores the challenges of diagnostics and prognostics in high-dimensional spatial-temporal data, extending from physical structures to complex human brain analyses through resting-state functional magnetic resonance imaging (rs-fMRI). Drawing an analogy to engineering structural health monitoring using spatial-temporal vibration data, the approach leverages techniques from engineering diagnostics and prognostics data analytics to handle clinical problems with similar characteristics. A pioneering approach is developed to analyze multimodal datasets that not only include advanced rs-fMRI features—Amplitude of Low-frequency Fluctuations (ALFF), Regional Homogeneity (ReHo), Euler Characteristics (EC), and Fractal Analysis—but also encompass a wide array of clinical data. This integration includes infant developmental metrics such as birth weight and gestational age, maternal health factors like BMI and fat mass, and environmental influences including dietary intake and mental health during pregnancy. The study establishes a robust computational framework that uses advanced machine learning algorithms to analyze the interplay of these diverse data types, enhancing the precision and predictive power of our models for early childhood development. Initial validations have demonstrated the effectiveness of this comprehensive approach in identifying ADHD, with ongoing efforts aimed at expanding the methodology to address a broader range of developmental disorders. This work not only advances the diagnostic and prognostic capabilities in medical imaging but also significantly contributes to the field of Prognostics and Health Management (PHM). By providing a solid foundation for managing and understanding high-dimensional and multimodal spatial-temporal data across various disciplines, it bridges the gap between engineering and clinical diagnostics, demonstrating the potential for cross-disciplinary innovation.

On most informative regions for binary classification of schizophrenia based on resting state fMRI data done by selection of functionally homogeneous regions method

In this work we solve the problem of automatic binary classification of subjects with a diagnosis of schizophrenia and control groups on a data set obtained on a Siemens 3T tomograph. The data set included 36 subjects undergoing treatment at Psychiatric Hospital no. 1 Named after N.A. Alexeev of the Department of Health of Moscow (GBUZ PKB No. 1 DZM) and 36 subjects from the control group. Machine learning methods were used to solve this problem. As a result, an accuracy of 76% was achieved, which corresponds to the results obtained in other scientific studies. The highest accuracy was obtained for the local homogeneity parameter (regional homogeneity – ReHo), already known in the literature. At the same time, the set of features developed by the authors based on the method for identifying functionally homogeneous regions (FHR) gave a classification accuracy of 74%. But at the same time, the set of FHR features provides higher classification accuracy when using a small number of brain regions. For example, already in 8 regions, the FHR set provided an almost maximum classification accuracy of 72.5% (versus 65% for the ReHo set), which suggests that it is the selected 8 regions that give the highest level of separation.

Brain functional alternation in patients with systemic sclerosis: a resting-state functional magnetic resonance imaging study

BackgroundNeuropsychiatric manifestations, such as cognitive impairment, are relatively prevalent in systemic sclerosis (SSc) patients. This study aimed to investigate the resting state (RS) functional alternations of SSc patients and the potential influenced factors.MethodsForty-four SSc patients (mean age, 46.3 ± 11.4 years; 40 females) and 19 age and sex comparable healthy volunteers (mean age, 42.6 ± 11.3 years; 16 females) were recruited and underwent RS functional MR imaging (fMRI) and neuropsychological assessments. Functional segregation analysis was performed to calculate the amplitude of low frequency fluctuation (ALFF) and regional homogeneity (ReHo). Functional integration analysis was conducted using group independent component analysis to calculate intra-network and inter-network functional connectivity (FC). The fMRI measurements were compared between SSc patients and healthy volunteers using voxel-based pairwise two-sample t-tests. The correlations between clinical characteristics and fMRI measurements were also analyzed.ResultsCompared to healthy volunteers, SSc patients exhibited significantly decreased ALFF and increased ReHo (all P < 0.01, FWE corrected). SSc patients predominantly showed decreased intra-network and inter-network FC in the auditory network, visual network, default mode network, frontoparietal network and attention network (intra-network FC: P < 0.01, uncorrected, cluster size > 30; inter-network FC: P < 0.05, FDR correction). Furthermore, clinical characteristics including disease duration (r value ranged from − 0.31 to 0.36), elevated erythrocyte sedimentation rate (r = 0.35), Montreal Cognitive Assessment score (r = 0.43), and Hamilton Depression Scale score (r = -0.40) were significantly associated with fMRI measurements (all P < 0.05).ConclusionsSpontaneous activity and functional connectivity alternations can be seen in SSc patients, which are partially associated with neuropsychiatric manifestations and tend to aggravate with disease duration.

Impact of shifting patterns of the South Asian High on interannual variability of Indian summer monsoon rainfall in homogeneous regions

AbstractThe movement and intensity indices of the South Asian High (SAH), an upper‐level anticyclonic‐circulation over the Tibetan Plateau, play a crucial role in Indian summer monsoon rainfall (ISMR) during June–September. The present study aims to document (i) the association between SAH and ISMR in the periods of June–July–August (JJA) and July–August–September (JAS), and (ii) the ISMR changes at different spatial and temporal scales. The Bayesian change‐point detection method identifies 1981 as change point and the dataset has been split into two groups (past climate:1940–1980 and current climate:1981–2020) to analyze temporal variations in ISMR. Results indicate that the northwest–southeast index (INW–SE), north–south index (INS) and intensity index (IINT) of the SAH are strongly correlated (˜0.67, ˜0.60, and ˜0.51, respectively) with ISMR whereas the east–west index (IEW) is negatively correlated (˜−0.52) during the JAS season. This relation was stronger in the past climate than the current climate, except for the IINT index during the JAS season. The INW–SE and INS indices are closely associated with all‐India, northwest India (NWI), and central India (CI) rainfall, whereas IINT is associated with south peninsular India (SPI) during the JAS season. The increased rainfall over NWI and SPI in the current climate is strongly associated with positive INS and IINT indices, respectively, during the JAS season. The northeast India (NEI) rainfall did not show any association with SAH indices; however, the IEW is strongly associated with increased NEI rainfall during El Niño years. A significant positive (negative) relation between meridional (zonal) wind shear and SAH indices except the IEW index is observed. Further, the positive (negative) SAH indices favor more (moderate) ISMR due to strong positive (negative) moisture anomalies. The study demonstrates the movement and intensity of the SAH in the dynamical shifts of seasonal rainfall patterns across the Indian homogeneous regions.

Wave Energy Potential and the Role of Extreme Events on South America's Coasts. A Regional Frequency Analysis

This study examines wave energy potential and generation capacity from extreme waves along South America's Pacific and Atlantic coasts. Utilizing the Regional Frequency Analysis (RFA) method and the WAVERYS wave reanalysis model, 27 homogeneous regions with distinct wave patterns were identified. There's a notable southward increase in median and average wave heights and energy. Unlike previous studies, this research emphasizes the significant role of extreme wave events, which contribute up to 33 % of the total energy—a critical factor often overlooked in regional energy assessments. In the Pacific, three main zones were delineated, with energy peaks reaching 120 kW/m south of 50°S, while the Atlantic identified two zones with energy values ranging from 10 kW/m to 30 kW/m. Zones between 10°S to 40°S in the Pacific and 30°S to 60°S in the Atlantic stand out as particularly promising for energy harvesting. A comprehensive statistical methodology enabled predictions of wave heights and energy for 50 and 100-year return periods, predominantly utilized generalized Pareto and Gumbel distributions. The study affirms the efficacy of the RFA in improving the understanding of wave dynamics and highlights the necessity of integrating extreme events in energy assessments. This research provides a foundation for advancing wave energy initiatives and coastal risk mitigation in South America.

Association of inflammatory cytokines with magnetic resonance imaging features of the brain in patients with depression.

There is growing evidence that the imbalance of inflammatory cytokines plays an important role in the pathophysiological mechanism of depression. However, the effects of inflammatory cytokines on the whole brain in patients with depression are still not fully elucidated. The present study aimed to investigate the relationship between inflammatory cytokines and cerebral magnetic resonance imaging (MRI) features using voxel-based whole-brain analysis in patients with depression. A total of 60 patients with depression and 60 healthy controls (HCs) were included. Interleukin-1 was positively correlated with gray matter volume (GMV) in the left putamen and negatively correlated with regional homogeneity (ReHo) and degree centrality (DC) in the left anterior cingulate cortex. Interleukin-6 was positively correlated with GMV in the right superior parietal lobule and ReHo in the left pallidum and putamen. Interferon-α was negatively correlated with DC in the left postcentral gyrus. The ReHo in the left pallidum in depressed patients was lower than that in HCs. The FCs based on the left pallidum as the seed in depressed patients were significantly reduced. The imaging features of the left pallidum had good performance (area under the curve: 0.891) for identifying depressed patients. Inflammatory cytokines are associated with cerebral imaging features in patients with depression and in particular, the abnormal imaging features of the left pallidum may be a potential neuroimaging biomarker of depression.

Extending multi-criteria coastal vulnerability assessment to low-lying inland areas: examples from Estonia, eastern Baltic Sea

The assessment of vulnerability to coastal hazards is a significant coastal management problem in regions with complicated shoreline, such as Estonia. This study implements the vulnerability assessment based on the multi-criteria decision analysis using fuzzy logic, analytical hierarchy process, and weighted linear combination (including input from experts) integrated with a geographical information system, to map the coastal vulnerability index (CVI) of the Estonian coasts at high resolution based on 16 parameters. The novelty of our approach is that we expand this assessment to a 2 km wide inland area that is an intrinsic but often overlooked part of coastal vulnerability estimates. The Estonian shores have mostly low and moderate vulnerability. Short segments with high vulnerability are impacted by severe waves and highly elevated water levels. The CVI also characterizes low-lying areas, such as large river valleys, reasonably well. Estimates of coastal vulnerability based on the three most important parameters according to experts’ judgements provide a reasonable approximation of the 16-parameter CVI in mostly homogeneous coastal regions, but less so elsewhere where its value is questioned. The results show that the application of the developed integrated decision support system, applied to a 2 km wide coastal strip, provides more information than single tools to assist coastal managers and stakeholders in planning, preparing for and responding to hazards.

Análise de cluster e regionalização hidrológica para Estados brasileiros

ABSTRACT Streamflow data from gauging stations are essential for effective water resources management. However, some regions in Brazil lack the necessary data. Hydrological regionalization is an alternative technique for obtaining data such regions. However, not all regions in Brazil have defined hydrological regionalization models, including the state of Goiás and the Brazilian Federal District. The objective of this study was to develop a hydrological regionalization methodology based on the separation of hydrologically homogeneous regions and multiple linear regression, using a Geographic Information System (GIS) program. Historical series data were used to calculate reference flows with 90 or 95% duration over time in the watercourse (Q90 and Q95) and the mean flow (Ǭ). Rain gauge station data were used to calculate the mean annual rainfall in each watershed through spatial interpolation by ordinary kriging. Subsequently, the physiographic characteristics of each watershed were calculated. The hydrologically homogeneous regions were delimited based on these data using cluster analysis, which identified seven hydrologically homogeneous regions in Goiás, two of them belonging to the Federal District. Multiple regression allowed the development of seven regionalization models. Models for regions 1, 3, 4, 5, and 7 showed better performance.

White Matter Function and Network Abnormalities in Patients with Diabetic Retinopathy.

This study aims to explore changes in white matter function and network connectivity in individuals with DR. This study included 46 patients with DR and 43 age- and gender-matched healthy control (HC) participants were enrolled in the study. The aim was to investigate inter-group differences in white matter (WM) function and to analyze changes in the WM network among DR patients. Increased degree centrality (DC) values were observed in the middle cerebellar peduncle and genu of the corpus callosum, while higher fractional amplitude of low-frequency fluctuations (fALFF) values were found in the left superior corona radiata, right anterior corona radiata, and right superior longitudinal fasciculus. Conversely, reduced regional homogeneity (ReHo) values were noted in the left posterior thalamic radiation among patients with DR compared to HC, with statistical correction applied The SVM classification accuracy for distinguishing between DR and HC patients based on WM measures indicated values of 81.52%, 80.43%, and 89.13% for DC, fALFF, and ReHo, respectively, with respective area under the curve (AUC) values of 0.87, 0.85, and 0.93. Furthermore, alterations were detected within specific brain regions including the body of corpus callosum (BCC), splenium of corpus callosum (SCC), genu of corpus callosum (GCC), left posterior thalamic radiation (PTR), right anterior corona radiata (ACR), and right posterior corona radiata (PCR) in the DR group compared to HCs, with an intra-network decrease in connectivity. Interestingly, the left superior longitudinal fasciculus (SLF) within the DR group exhibited an intra-network increase compared to the HC group. DR exhibited abnormal white matter functional alterations, particularly affecting the fiber pathways linking the visual network to the sensory-motor network.

Alterations of regional homogeneity and functional connectivity in different hoehn and yahr stages of Parkinson's disease

PurposeUsing regional homogeneity (ReHo) and functional connectivity (FC) to assess alterations in brain function and their potential relation to different Hoehn and Yahr (H&Y) stages in Parkinson's disease (PD). Materials and methods66 patients with PD and 57 age- and sex-matched healthy control (HC) participants were recruited. All subjects underwent clinical assessments and resting-state functional magnetic resonance imaging (rs-fMRI) scanning. We analyzed alterations in regional brain activity using ReHo analyses in all subjects and further explored their relationship to disease severity. Finally, the brain region significantly associated with disease severity was used as a seed point to analyze the FC changes between it and other brain regions in the whole brain. ResultsCompared with HC participants, PD patients showed a significant decrease ReHo in the sensorimotor network (bilateral precentral and postcentral gyrus). The ReHo value of the left precentral gyrus in PD patients decreased with increasing H&Y stage and correlated negatively with Unified Parkinson’s Disease Rating Scale (UPDRS) III scores. Further, FC analysis of the left precentral gyrus as a region of interest showed that functional activity between the left precentral gyrus and sensorimotor network, default network, and visual network was decreased. ConclusionThe left precentral gyrus plays an important role in the pathophysiological mechanisms of PD patients, and this finding further highlights the potential of the primary motor cortex (M1) as a non-invasive therapeutic target for neuromodulation in PD patients.

Regional frequency analysis of extreme wind in Pakistan using robust estimation methods

The quantile estimates of extreme wind speed are needed for various areas of interest using regional frequency analysis (RFA) and extreme value theory. These calculations are crucial for the coding of wind speed. The data was taken from the NASA official website at a 10-meter distance and measured in meters per second (m/s). RFA of annual maximum wind speed (AMWS) using L-moments is performed utilizing annual maximum wind speed data from sixteen sites (16) in Pakistan’s Khyber Pakhtunkhwa province. There are no sites that are found to be discordant. The wards method is used to construct a homogenous region and make two homogenous regions from 16 sites. The heterogeneity test justifies that both clusters are homogeneous. The most appropriate probability distribution from the Generalized Normal (GNO), Generalized Logistic (GLO), Pearson Type-3 (P3), Generalized Pareto (GPA), and Generalized Extreme Value (GEV) distributions is chosen to calculate regional quantiles. According to the L-moments diagram and Z statistics, the GEV for Cluster- Ι and GLO for Cluster- ΙΙ are the best suggestions from the others. Both clusters’ robustness is measured utilizing relative bias (RB) and relative root mean square error (RRMSE). Overall, GEV distribution is fit for cluster-Ι, and the GLO distribution is fit for cluster-ΙΙ. Utilizing the site mean and median as index parameters, we can also find at-site quantiles from regional quantiles. The study’s quantile estimates can be employed in codified structural designs with policy consequences.

Integrated Analysis of Gut microbiome, Inflammation and Neuroimaging Features Supports the Role of Microbiome-Gut-Brain Crosstalk in Schizophrenia

Abstract Background and Hypothesis Gut microbiota has been implicated in the pathogenesis of schizophrenia (SZ) and relevant changes in the brain, but the underlying mechanism remains elusive. This study aims to investigate the microbiota-gut-brain crosstalk centered on peripheral inflammation in SZ patients. Study Design We recruited a cohort of 182 SZ patients and 120 healthy controls (HC). Multi-omics data, including fecal 16S rRNA, cytokine data, and neuroimaging data, were collected and synthesized for analysis. Multi-omics correlations and mediation analyses were utilized to determine the associations of gut microbiome with inflammatory cytokines and neuroimaging characteristics. Additionally, machine learning models for effective SZ diagnosis were separately generated based on gut microbial and neuroimaging data. Study Results Gut microbial dysbiosis, characterized by a decrease in butyrate-producing bacteria and an increase in pro-inflammatory bacteria, has been identified in SZ patients. These key microbial taxa were associated with increased inflammatory cytokines, potentially through mediating lipid metabolic pathways such as steroid biosynthesis and linoleic acid metabolism. Further analysis revealed altered microbial genera to be correlated with disrupted gray matter volume and regional homogeneity in SZ patients. Importantly, certain inflammatory cytokines mediated the relationship between the SZ-enriched genus Succinivibrio and aberrant activity of anterior cingulate cortex and left inferior temporal gyrus in the SZ group. Moreover, the classification model based on gut microbial data showed comparable efficacy to the model based on brain functional signatures in SZ diagnosis. Conclusions This study presents evidence for the dysregulated microbiota-gut-brain axis in SZ and emphasizes the central role of peripheral inflammation.

EXPLORING CLIMATIC PATTERNS AMONG BRAZILIAN CAPITALS

This study aims to examine the patterns of climatic variables and delineate climatically homogeneous regions among Brazilian capitals using Principal Component Analysis (PCA) and clustering techniques. Annual climate data from 26 state capitals and the Federal District of Brazil were analyzed, covering the period from 1958 to 2022, representing different regions of the country. The PCA results revealed that the first three principal components accounted for 91.8% of the total data variability. Four climatically homogeneous regions were identified: the East region, the Northeast region, the North region and another region that comprises the rest of the country. Cluster Analysis (CA) also confirmed the formation of four homogeneous groups among municipalities based on climatic variables. The analysis of spatial distribution showed significant variations in annual climate variables between Brazilian capitals, with maximum values observed for maximum and minimum temperatures, precipitation, wind speed, relative humidity, evapotranspiration and soil moisture. These findings contribute to a deeper understanding of climate variability in Brazilian capitals and provide a basis for future studies on climate dynamics and regional impacts. Keywords: Principal Component Analysis, Cluster Analysis, Multivariate Analysis; Biplot

Predictive and Explainable Artificial Intelligence for Neuroimaging Applications.

The aim of this review is to highlight the new advance of predictive and explainable artificial intelligence for neuroimaging applications. Data came from 30 original studies in PubMed with the following search terms: "neuroimaging" (title) together with "machine learning" (title) or "deep learning" (title). The 30 original studies were eligible according to the following criteria: the participants with the dependent variable of brain image or associated disease; the interventions/comparisons of artificial intelligence; the outcomes of accuracy, the area under the curve (AUC), and/or variable importance; the publication year of 2019 or later; and the publication language of English. The performance outcomes reported were within 58-96 for accuracy (%), 66-97 for sensitivity (%), 76-98 for specificity (%), and 70-98 for the AUC (%). The support vector machine and the convolutional neural network registered the best performance (AUC 98%) for the classifications of low- vs. high-grade glioma and brain conditions, respectively. Likewise, the random forest delivered the best performance (root mean square error 1) for the regression of brain conditions. The following factors were discovered to be major predictors of brain image or associated disease: (demographic) age, education, sex; (health-related) alpha desynchronization, Alzheimer's disease stage, CD4, depression, distress, mild behavioral impairment, RNA sequencing; (neuroimaging) abnormal amyloid-β, amplitude of low-frequency fluctuation, cortical thickness, functional connectivity, fractal dimension measure, gray matter volume, left amygdala activity, left hippocampal volume, plasma neurofilament light, right cerebellum, regional homogeneity, right middle occipital gyrus, surface area, sub-cortical volume. Predictive and explainable artificial intelligence provide an effective, non-invasive decision support system for neuroimaging applications.

Multimodal and quantitative analysis of the epileptogenic zone network in the pre-surgical evaluation of drug-resistant focal epilepsy

Surgical resection for epilepsy often fails due to incomplete Epileptogenic Zone Network (EZN) localization from scalp electroencephalography (EEG), stereo-EEG (SEEG), and Magnetic Resonance Imaging (MRI). Subjective interpretation based on interictal, or ictal recordings limits conventional EZN localization. This study employs multimodal analysis using high-density-EEG (HDEEG), Magnetoencephalography (MEG), functional-MRI (fMRI), and SEEG to overcome these limitations in a patient with drug-resistant MRI-negative focal epilepsy. A 17-year-old with drug-resistant epilepsy underwent evaluation. HDEEG, MEG, fMRI, and SEEG were used, with a novel HDEEG-cap facilitating simultaneous EEG-MEG and EEG-fMRI recordings. Electrical and magnetic source imaging were performed, and fMRI data were analysed for homogenous regions. SEEG analysis involved spike detection, spike timing analysis, ictal fast activity quantification, and Granger-based connectivity analysis. Non-invasive sessions revealed consistent interictal source imaging results identifying the EZN in the right anterior cingulate cortex. EEG-fMRI highlighted broader activation in the right cingulate cortex. SEEG analysis localized spikes and fast activity in the right anterior and posterior cingulate gyri. Multi-modal analysis suggested the EZN in the right frontal lobe, primarily involving the anterior and mid-cingulate cortices. Multi-modal non-invasive analyses can optimise SEEG implantation and surgical decision-making. Invasive analyses corroborated non-invasive findings, emphasising the importance of individual-case quantitative analysis across modalities in complex epilepsy cases.

Local heterogenisation and regional homogenisation linked to habitat loss induced by dams in riparian forests of the Brazilian Atlantic Forest

Riparian forests are crucial for biodiversity, but dam construction for hydroelectric power disrupts these ecosystems, causing habitat loss and altering river dynamics. Our study investigates the impacts of dams on tree diversity in the southern Brazilian Atlantic Forest. We sampled trees along riverbanks and uplands across 15 dam-affected fragments, analysing the relationship between habitat loss (i.e. loss of riparian zones by permanent flooding due to dam filling), elevation difference, fragment size, and dam implementation time with alpha and beta diversity using mixed models and redundancy analyses. Habitat loss had a more significant impact on beta diversity, leading to shifts in species composition and reduced uniqueness of communities as the impact’s intensity, spatial extent, and duration increased. Alpha diversity only increased in response to local elevation differences between plots located on uplands and riverbanks. Our sampling design can be applied to other inadequately monitored systems to provide insights into beta diversity, a component often neglected in dam licensing and mitigation processes. Our findings reveal a transient local heterogenisation, transitioning into regional homogenisation due to dam-induced habitat loss in riparian forests of the Brazilian Atlantic Forest.

Functional magnetic resonance imaging of depression: a bibliometrics and meta-analysis

ObjectivesThis study aims to reveal the current knowledge map, research hotspots of functional magnetic resonance imaging (fMRI) studies on depression, as well as identify the brain regions associated with depression.MethodsCiteSpace was conducted to analyze the publication outputs, country, institution, cited journals, author and cited author, references, keyword cocurrence and burst keywords of fMRI studies in depression from 2010 to 2024. And a meta-analysis of fMRI was used to identify brain regions associated with depression using Neurosynth.ResultsA total of 4,049 publications were included, and Gong Qiyong was the most prolific authors. Neuroimage, Biological Psychiatry, and Human Brain Mapping were prominent journals. Default mode network (DMN), prefrontal cortex, amygdala, and anterior cingulate cortex were the popular keywords. The fMRI studies on depression have mainly focused on major depression, especially the DMN. Functional connectivity and regional homogeneity of brain regions were research hotspots. The meta-analysis revealed significant differences in brain regions between patients with depression and healthy controls, including the Amygdala_L, Insula_R, Frontal_Inf_Oper_R, Cingulum_Post_L, Putamen_L, Thalamus_R, Angular_L, Precuneus_R, Frontal_Sup_R, Occipital_Inf_L.ConclusionsThis study sheds light on key issues and future directions in fMRI research on depression, elucidating the brain regions related to depression.Graphical