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BACKGROUND: Anhedonia is a core symptom of depression that is closely related to prognosis and treatment outcomes. However, accurate and efficient treatments for anhedonia are lacking, mandating a deeper understanding of the underlying mechanisms. METHODS: A total of 303 patients diagnosed with depression and anhedonia were assessed by the Snaith-Hamilton Pleasure Scale (SHAPS) and magnetic resonance imaging (MRI). The patients were categorized into a low-anhedonia group and a high-anhedonia group using the K-means algorithm. A data-driven approach was used to explore the differences in brain structure and function with different degrees of anhedonia based on MATLAB. A random forest model was used exploratorily to test the predictive ability of differences in brain structure and function on anhedonia in depression. RESULTS: Structural and functional differences were apparent in several brain regions of patients with depression and high-level anhedonia, including in the temporal lobe, paracingulate gyrus, superior frontal gyrus, inferior occipital gyrus, right insular gyrus, and superior parietal lobule. And changes in these brain regions were significantly correlated with scores of SHAPS. CONCLUSIONS: These brain regions may be useful as biomarkers that provide a more objective assessment of anhedonia in depression, laying the foundation for precision medicine in this treatment-resistant, relatively poor prognosis group.
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Anhedonia , Encéfalo , Imagen por Resonancia Magnética , Humanos , Anhedonia/fisiología , Femenino , Masculino , Adulto , Persona de Mediana Edad , Encéfalo/diagnóstico por imagen , Encéfalo/fisiopatología , Neuroimagen , Trastorno Depresivo Mayor/diagnóstico por imagen , Trastorno Depresivo Mayor/fisiopatología , Depresión/fisiopatología , Depresión/diagnóstico por imagen , Escalas de Valoración Psiquiátrica , Lóbulo Temporal/diagnóstico por imagen , Lóbulo Temporal/fisiopatologíaRESUMEN
In the agricultural sector, farmers and agribusiness are confronted with a multitude of complex choices every day. These selections are influenced by multiple variables that significantly affect their outcomes. The primary source of revenue for a good deal of individuals is derived from the agricultural sector. The provision of precise and punctual predictions on crop yields has significant importance in facilitating informed investment choices and shaping agricultural policies. One of the challenges encountered is the presence of old or incomplete data about the accessibility of resources. This represents a significant obstacle in accurately ascertaining the present state of affairs. The process of evaluating becomes complex as a result of the diverse range of soil conditions and climatic factors. This research introduces a novel approach called Enhanced Gravitational Search Optimized based Gated Recurrent Unit (EGSO-GRU) for the purpose of calculating crop production. The dataset was first gathered and pre-processed using a normalization method. Enhanced independent component analyses (EICA) have been employed for the purpose of extracting features. To determine the suggest method achievement with regard to accuracy (95.89%), specificity (92.4%), MSE (0.071), RMSE (0.210) and MAE (0.199). The proposed method achieved greater crop prediction accuracy, outperforming the majority of the existing models. The necessity of this progress is vital to the successful operation of crops. The concept signifies a technological advancement aimed at optimizing agricultural resources, hence fostering enhanced productivity and long-term sustainability within the farming industry.
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In Parkinson's disease (PD) functional changes in the brain occur years before significant cognitive symptoms manifest yet core large-scale networks that maintain cognition and predict future cognitive decline are poorly understood. The present study investigated internetwork functional connectivity of visual (VN), anterior and posterior default mode (aDMN, pDMN), left/right frontoparietal (LFPN, RFPN), and salience (SN) networks in 63 cognitively normal PD (PDCN) and 43 healthy controls who underwent resting-state functional MRI. The functional relevance of internetwork coupling topologies was tested by their correlations with baseline cognitive performance in each group and with 2-year cognitive changes in a PDCN subsample. To disentangle heterogeneity in neurocognitive functioning, we also studied whether α-synuclein (SNCA) and microtubule-associated protein tau (MAPT) variants alter internetwork connectivity and/or accelerate cognitive decline. We found that internetwork connectivity was largely preserved in PDCN, except for reduced pDMN-RFPN/LFPN couplings, which correlated with poorer baseline global cognition. Preserved internetwork couplings also correlated with domain-specific cognition but differently for the two groups. In PDCN, stronger positive internetwork coupling topologies correlated with better cognition at baseline, suggesting a compensatory mechanism arising from less effective deployment of networks that supported cognition in healthy controls. However, stronger positive internetwork coupling topologies typically predicted greater longitudinal decline in most cognitive domains, suggesting that they were surrogate markers of neuronal vulnerability. In this regard, stronger aDMN-SN, LFPN-SN, and/or LFPN-VN connectivity predicted longitudinal decline in attention, working memory, executive functioning, and visual cognition, which is a risk factor for dementia. Coupling strengths of some internetwork topologies were altered by genetic variants. PDCN carriers of the SNCA risk allele showed amplified anticorrelations between the SN and the VN/pDMN, which supported cognition in healthy controls, but strengthened pDMN-RFPN connectivity, which maintained visual memory longitudinally. PDCN carriers of the MAPT risk allele showed greater longitudinal decline in working memory and increased VN-LFPN connectivity, which in turn predicted greater decline in visuospatial processing. Collectively, the results suggest that cognition is maintained by functional reconfiguration of large-scale internetwork communications, which are partly altered by genetic risk factors and predict future domain-specific cognitive progression.
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BACKGROUND: A number of studies in recent years have explored whole-brain dynamic connectivity using pairwise approaches. There has been less focus on trying to analyze brain dynamics in higher dimensions over time. METHODS: We introduce a new approach that analyzes time series trajectories to identify high traffic nodes in a high dimensional space. First, functional magnetic resonance imaging (fMRI) data are decomposed using spatial ICA to a set of maps and their associated time series. Next, density is calculated for each time point and high-density points are clustered to identify a small set of high traffic nodes. We validated our method using simulations and then implemented it on a real data set. RESULTS: We present a novel approach that captures dynamics within a high dimensional space and also does not use any windowing in contrast to many existing approaches. The approach enables one to characterize and study the time series in a potentially high dimensional space, rather than looking at each component pair separately. Our results show that schizophrenia patients have a lower dynamism compared to healthy controls. In addition, we find patients spend more time in nodes associated with the default mode network and less time in components strongly correlated with auditory and sensorimotor regions. Interestingly, we also found that subjects oscillate between state pairs that show opposite spatial maps, suggesting an oscillatory pattern. CONCLUSION: Our proposed method provides a novel approach to analyze the data in its native high dimensional space and can possibly provide new information that is undetectable using other methods.
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[This corrects the article DOI: 10.3389/fnins.2019.01334.].
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BACKGROUND AND OBJECTIVES: The underlying neuropathology of excessive daytime sleepiness (EDS) remains elusive in Parkinson's disease (PD). We aim to investigate neural network changes that underlie EDS in PD. METHODS: Early PD patients comprising eighty-one patients without EDS (EDS-) and seventeen patients with EDS (EDS+) received a resting state functional MRI scan and the Epworth Sleepiness Scale (ESS). Connectivities within the default mode network (DMN), motor and basal ganglia networks were compared between the EDS+ and EDS- groups. Correlations between network connectivity and the severity of EDS were investigated through linear regression. RESULTS: EDS+ patients displayed a trend of increased network connectivity of the posterior DMN (pDMN). A significant positive correlation was found between connectivity of the ventromedial prefrontal cortex in the pDMN and ESS. CONCLUSION: EDS+ patients are likely to display increased activation in the DMN, suggesting neural compensation in early PD or impaired attentiveness due to mechanisms such as mind-wandering.
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Reductions in brain volumes represent a neurobiological signature of fetal alcohol spectrum disorders (FASD). Less clear is how regional brain tissue reductions differ after normalizing for brain size differences linked with FASD and whether these profiles can predict the degree of prenatal exposure to alcohol. To examine associations of regional brain tissue excesses/deficits with degree of prenatal alcohol exposure and diagnosis with and without correction for overall brain volume, tensor-based morphometry (TBM) methods were applied to structural imaging data from a well-characterized, demographically homogeneous sample of children diagnosed with FASD (n = 39, 9.6-11.0 years) and controls (n = 16, 9.5-11.0 years). Degree of prenatal alcohol exposure was significantly associated with regionally pervasive brain tissue reductions in: (1) the thalamus, midbrain, and ventromedial frontal lobe, (2) the superior cerebellum and inferior occipital lobe, (3) the dorsolateral frontal cortex, and (4) the precuneus and superior parietal lobule. When overall brain size was factored out of the analysis on a subject-by-subject basis, no regions showed significant associations with alcohol exposure. FASD diagnosis was associated with a similar deformation pattern, but few of the regions survived FDR correction. In data-driven independent component analyses (ICA) regional brain tissue deformations successfully distinguished individuals based on extent of prenatal alcohol exposure and to a lesser degree, diagnosis. The greater sensitivity of the continuous measure of alcohol exposure compared with the categorical diagnosis across diverse brain regions underscores the dose dependence of these effects. The ICA results illustrate that profiles of brain tissue alterations may be a useful indicator of prenatal alcohol exposure when reliable historical data are not available and facial features are not apparent.
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Encéfalo/patología , Trastornos del Espectro Alcohólico Fetal/patología , Niño , Femenino , Humanos , Procesamiento de Imagen Asistido por Computador , Imagen por Resonancia Magnética , Masculino , Pruebas Neuropsicológicas , Tamaño de los Órganos/fisiologíaRESUMEN
Resting-state functional magnetic resonance imaging (rs-fMRI) has been shown to elucidate reliable patterns of brain networks in both children and adults. Studies in adults have shown that rs-fMRI acquisition times of â¼5 to 6 min provide adequate sampling to produce stable spatial maps of a number of different brain networks. However, it is unclear whether the acquisition time directly translates to studies of children. While there are many similarities between the brains of children and adults, many differences are also evident. Children have increased metabolism, differences in brain morphology and connectivity strengths, greater brain plasticity, and increased brain noise. Furthermore, there are differences in physiologic parameters, such as heart and respiratory rates, and compliance of the blood vessels. These developmental differences could translate into different acquisition times for rs-fMRI studies in pediatric populations. Longer scan times, however, increase the subject burden and the risk for greater movement, especially in children. Thus, the goal of this study was to assess the optimum acquisition time of rs-fMRI to extract stable brain networks in school-age children. We utilized fuzzy set theory in 84 six-to-eight year-old children and found that eight networks, including the default mode, salience, frontal, left frontoparietal, right frontoparietal, sensorimotor, auditory, and visual networks, all stabilized after â¼5½ min. The sensorimotor network showed the least stability, whereas the salience and auditory networks showed the greatest stability. A secondary analysis using dual regression confirmed these results. In conclusion, in young children with little head motion, rs-fMRI acquisition times of â¼5½ min can extract the full complement of brain networks.