Vulnerable brain regions in adolescent major depressive disorder:A resting-state functional magnetic resonance imaging activation likelihood estimation meta-analysis

BACKGROUND Adolescent major depressive disorder(MDD)is a significant mental health concern that often leads to recurrent depression in adulthood.Resting-state functional magnetic resonance imaging(rs-fMRI)offers unique insights into the neural mechanisms underlying this condition.However,despite previous research,the specific vulnerable brain regions affected in adolescent MDD patients have not been fully elucidated.AIM To identify consistent vulnerable brain regions in adolescent MDD patients using rs-fMRI and activation likelihood estimation(ALE)meta-analysis.METHODS We performed a comprehensive literature search through July 12,2023,for studies investigating brain functional changes in adolescent MDD patients.We utilized regional homogeneity(ReHo),amplitude of low-frequency fluctuations(ALFF)and fractional ALFF(fALFF)analyses.We compared the regions of aberrant spontaneous neural activity in adolescents with MDD vs healthy controls(HCs)using ALE.RESULTS Ten studies(369 adolescent MDD patients and 313 HCs)were included.Combining the ReHo and ALFF/fALFF data,the results revealed that the activity in the right cuneus and left precuneus was lower in the adolescent MDD patients than in the HCs(voxel size:648 mm3,P<0.05),and no brain region exhibited increased activity.Based on the ALFF data,we found decreased activity in the right cuneus and left precuneus in adolescent MDD patients(voxel size:736 mm3,P<0.05),with no regions exhibiting increased activity.CONCLUSION Through ALE meta-analysis,we consistently identified the right cuneus and left precuneus as vulnerable brain regions in adolescent MDD patients,increasing our understanding of the neuropathology of affected adolescents.

Activation Functions Effect on Fractal Coding Using Neural Networks

Activation functions play an essential role in converting the output of the artificial neural network into nonlinear results,since without this nonlinearity,the results of the network will be less accurate.Nonlinearity is the mission of all nonlinear functions,except for polynomials.The activation function must be dif-ferentiable for backpropagation learning.This study’s objective is to determine the best activation functions for the approximation of each fractal image.Different results have been attained using Matlab and Visual Basic programs,which indi-cate that the bounded function is more helpful than other functions.The non-lin-earity of the activation function is important when using neural networks for coding fractal images because the coefficients of the Iterated Function System are different according to the different types of fractals.The most commonly cho-sen activation function is the sigmoidal function,which produces a positive value.Other functions,such as tansh or arctan,whose values can be positive or negative depending on the network input,tend to train neural networks faster.The coding speed of the fractal image is different depending on the appropriate activation function chosen for each fractal shape.In this paper,we have provided the appro-priate activation functions for each type of system of iterated functions that help the network to identify the transactions of the system.

Elevated brain temperature under severe heat exposure impairs cortical motor activity and executive function

Background:Excessive heat exposure can lead to hyperthermia in humans,which impairs physical performance and disrupts cognitive function.While heat is a known physiological stressor,it is unclear how severe heat stress affects brain physiology and function.Methods:Eleven healthy participants were subjected to heat stress from prolonged exercise or warm water immersion until their rectal temperatures(T_(re))attained 39.5℃,inducing exertional or passive hyperthermia,respectively.In a separate trial,blended ice was ingested before and during exercise as a cooling strategy.Data were compared to a control condition with seated rest(normothermic).Brain temperature(T_(br)),cerebral perfusion,and task-based brain activity were assessed using magnetic resonance imaging techniques.Results:T_(br)in motor cortex was found to be tightly regulated at rest(37.3℃±0.4℃(mean±SD))despite fluctuations in T_(re).With the development of hyperthermia,T_(br)increases and dovetails with the rising T_(re).Bilateral motor cortical activity was suppressed during high-intensity plantarflexion tasks,implying a reduced central motor drive in hyperthermic participants(T_(re)=38.5℃±0.1℃).Global gray matter perfusion and regional perfusion in sensorimotor cortex were reduced with passive hyperthermia.Executive function was poorer under a passive hyperthermic state,and this could relate to compromised visual processing as indicated by the reduced activation of left lateral-occipital cortex.Conversely,ingestion of blended ice before and during exercise alleviated the rise in both T_(re)and T_(bc)and mitigated heat-related neural perturbations.Conclusion:Severe heat exposure elevates T_(br),disrupts motor cortical activity and executive function,and this can lead to impairment of physical and cognitive performance.

Revealing the correlation between adsorption energy and activation energy to predict the catalytic activity of metal oxides for HMX using DFT

Traditional selection of combustion catalysis is time-consuming and labor-intensive.Theoretical calculation is expected to resolve this problem.The adsorption energy of HMX and O atoms on 13 metal oxides was calculated using DMol3,since HMX and O are key substances in decomposition process.And the relationship between the adsorption energy of HMX,O on metal oxides(TiO_(2),Al_(2)O_(3),PbO,CuO,Fe_(2)O_(3),Co_(3)O_(4),Bi_(2)O_(3),NiO)and experimental T30 values(time required for the decomposition depth of HMX to reach 30%)was depicted as volcano plot.Thus,the T30 values of other metal oxides was predicted based on their adsorption energy on volcano plot and validated by previous experimental data.Further,the adsorption energy of HMX on ZrO_(2)and MnO_(2)was predicted based on the linear relationship between surface energy and adsorption energy,and T30 values were estimated based on volcano plot.The apparent activation energy data of HMX/MgO,HMX/SnO_(2),HMX/ZrO_(2),and HMX/MnO_(2)obtained from DSC experiments are basically consistent with our predicted T30 values,indicating that it is feasible to predict the catalytic activity based on the adsorption calculation,and it is expected that these simple structural properties can predict adsorption energy to reduce the large quantities of computation and experiment cost.

Cortical activity in patients with high-functioning ischemic stroke during the Purdue Pegboard Test:insights into bimanual coordinated fine motor skills with functional near-infrared spectroscopy

After stroke,even high-functioning individuals may experience compromised bimanual coordination and fine motor dexterity,leading to reduced functional independence.Bilateral arm training has been proposed as a promising intervention to address these deficits.However,the neural basis of the impairment of functional fine motor skills and their relationship to bimanual coordination performance in stroke patients remains unclear,limiting the development of more targeted interventions.To address this gap,our study employed functional near-infrared spectroscopy to investigate cortical responses in patients after stroke as they perform functional tasks that engage fine motor control and coordination.Twenty-four high-functioning patients with ischemic stroke(7 women,17 men;mean age 64.75±10.84 years)participated in this cross-sectional observational study and completed four subtasks from the Purdue Pegboard Test,which measures unimanual and bimanual finger and hand dexterity.We found significant bilateral activation of the sensorimotor cortices during all Purdue Pegboard Test subtasks,with bimanual tasks inducing higher cortical activation than the assembly subtask.Importantly,patients with better bimanual coordination exhibited lower cortical activation during the other three Purdue Pegboard Test subtasks.Notably,the observed neural response patterns varied depending on the specific subtask.In the unaffected hand task,the differences were primarily observed in the ipsilesional hemisphere.In contrast,the bilateral sensorimotor cortices and the contralesional hemisphere played a more prominent role in the bimanual task and assembly task,respectively.While significant correlations were found between cortical activation and unimanual tasks,no significant correlations were observed with bimanual tasks.This study provides insights into the neural basis of bimanual coordination and fine motor skills in high-functioning patients after stroke,highlighting task-dependent neural responses.The findings also suggest that patients who exhibit better bimanual performance demonstrate more efficient cortical activation.Therefore,incorporating bilateral arm training in post-stroke rehabilitation is important for better outcomes.The combination of functional near-infrared spectroscopy with functional motor paradigms is valuable for assessing skills and developing targeted interventions in stroke rehabilitation.

Associations between residence and cognitive function among Chinese older adults: the mediating role of leisure activity

Background:Cognitive function is a current research hotspot,residence may be related to differences in cognitive function,and the mediating role of leisure activities are limited in Chinese research.This study used leisure activities as a mediating variable to investigate the mediating role of leisure activity between place of residence(city-town-rural)and cognitive function among Chinese older,this is where the innovation of the article comes in.Methods:Using cross-sectional data from the 2018 Chinese Longitudinal Healthy Longevity Survey,Pearson correlation analyses were employed to examine the relationships among various indicators.Mediation analyses were conducted using the SPSS PROCESS macro program,version 3.5,written by Hayes,to explore the mediating effects of leisure activity between place of residence and cognitive function in older adults.Results:A total of 10955 older adults were included in this study,with a mean age of(84.23±11.57)years.Among them,2739(24.8%)lived in the city,3627(33.1%)in town,and 4615(42.1%)in rural areas;their leisure activity score was(5.34±3.77),and their cognitive function score was(24.69±6.65).Place of residence,leisure activities,and cognitive function were significantly correlated(P<0.01).Using city as a reference,place of residence is negatively associated with cognitive function,and place of residence not only had a direct effect on cognitive function in older adults:town-cognitive function(effect=–0.399;95%confidence interval(CI)=(–0.685,–0.113));rural-cognitive function(effect=–0.42;95%CI=(–0.698,–0.141)).There were also indirect effects on cognitive function through the pathway of leisure activity:town-leisure activity-cognitive function(effect=–0.17;95%CI=(–0.246,–0.1)),rural-leisure activity-cognitive function(effect=–0.199;95%CI=(–0.272,–0.13)).Conclusion:Leisure activities play a partially mediating role between the impact of place of residence and cognitive function in Chinese older adults,and it is vital to pay attention to the impact of place of residence on the cognitive function of older adults in various aspects,and to increase the participation rate of older adults in leisure activities,which is beneficial to the prevention of cognitive decline and the protection of older adult’s physical and mental health.

Enhancing Proficiency in Quadratic Equations and Functions Through the MILAPlus Strategy

This study investigates the efficacy of the Mathematics Independent Learning Activity Practice and Play Unite Scheme(MILAPlus)as an instructional strategy to improve the proficiency levels of Grade 9 students in quadratic equations and functions through a study carried out at Quezon National High School.The research involved 116 Grade 9 students and utilized a quantitative approach,incorporating both pre-assessment and post-assessment measures.The research utilizes a quasi-experimental design,examining the academic performance of students before and after the introduction of MILAPlus.The pre-assessment establishes a baseline,and the subsequent post-assessment measures the impact of the instructional strategy.Statistical analyses,including t-tests,assess the significance of differences in mean scores and mean percentage scores,providing quantitative insights into the effectiveness of MILAPlus.Findings from the study revealed a statistically significant improvement in both mean scores and mean percentage scores after the utilization of MILAPlus,indicating enhanced proficiency in quadratic equations and functions.The Mean Proficiency Scores(MPS)also showed a substantial increase,demonstrating a marked improvement in overall proficiency levels among Grade 9 students.In light of the results,recommendations were given including the continued utilization of MILAPlus as an instructional strategy and aligning its development with prescribed learning competencies.Emphasizing the consistent adherence to policies and guidelines for MILAPlus implementation is suggested for sustaining positive effects on students’long-term performance in mathematics.This research contributes valuable insights into the practical application and effectiveness of MILAPlus within the context of Grade 9 mathematics education at Quezon National High School.

A Universal Activation Function for Deep Learning

Recently,deep learning has achieved remarkable results in fields that require human cognitive ability,learning ability,and reasoning ability.Activation functions are very important because they provide the ability of artificial neural networks to learn complex patterns through nonlinearity.Various activation functions are being studied to solve problems such as vanishing gradients and dying nodes that may occur in the deep learning process.However,it takes a lot of time and effort for researchers to use the existing activation function in their research.Therefore,in this paper,we propose a universal activation function(UA)so that researchers can easily create and apply various activation functions and improve the performance of neural networks.UA can generate new types of activation functions as well as functions like traditional activation functions by properly adjusting three hyperparameters.The famous Convolutional Neural Network(CNN)and benchmark datasetwere used to evaluate the experimental performance of the UA proposed in this study.We compared the performance of the artificial neural network to which the traditional activation function is applied and the artificial neural network to which theUA is applied.In addition,we evaluated the performance of the new activation function generated by adjusting the hyperparameters of theUA.The experimental performance evaluation results showed that the classification performance of CNNs improved by up to 5%through the UA,although most of them showed similar performance to the traditional activation function.

Nonlinear Activation Functions in CNN Based on Fluid Dynamics and Its Applications

The nonlinear activation functions in the deep CNN(Convolutional Neural Network)based on fluid dynamics are presented.We propose two types of activation functions by applying the so-called parametric softsign to the negative region.We use significantly the well-known TensorFlow as the deep learning framework.The CNN architecture consists of three convolutional layers with the max-pooling and one fullyconnected softmax layer.The CNN approaches are applied to three benchmark datasets,namely,MNIST,CIFAR-10,and CIFAR-100.Numerical results demonstrate the workability and the validity of the present approach through comparison with other numerical performances.

Coexistence and local Mittag–Leffler stability of fractional-order recurrent neural networks with discontinuous activation functions

In this paper, coexistence and local Mittag–Leffler stability of fractional-order recurrent neural networks with discontinuous activation functions are addressed. Because of the discontinuity of the activation function, Filippov solution of the neural network is defined. Based on Brouwer's fixed point theorem and definition of Mittag–Leffler stability, sufficient criteria are established to ensure the existence of (2k + 3)~n (k ≥ 1) equilibrium points, among which (k + 2)~n equilibrium points are locally Mittag–Leffler stable. Compared with the existing results, the derived results cover local Mittag–Leffler stability of both fractional-order and integral-order recurrent neural networks. Meanwhile discontinuous networks might have higher storage capacity than the continuous ones. Two numerical examples are elaborated to substantiate the effective of the theoretical results.

Multistability of delayed complex-valued recurrent neural networks with discontinuous real-imaginarytype activation functions

In this paper, the multistability issue is discussed for delayed complex-valued recurrent neural networks with discontinuous real-imaginary-type activation functions. Based on a fixed theorem and stability definition, sufficient criteria are established for the existence and stability of multiple equilibria of complex-valued recurrent neural networks. The number of stable equilibria is larger than that of real-valued recurrent neural networks, which can be used to achieve high-capacity associative memories. One numerical example is provided to show the effectiveness and superiority of the presented results.

Neural Networks on an FPGA and Hardware-Friendly Activation Functions

This paper describes our implementation of several neural networks built on a field programmable gate array (FPGA) and used to recognize a handwritten digit dataset—the Modified National Institute of Standards and Technology (MNIST) database. We also propose a novel hardware-friendly activation function called the dynamic Rectifid Linear Unit (ReLU)—D-ReLU function that achieves higher performance than traditional activation functions at no cost to accuracy. We built a 2-layer online training multilayer perceptron (MLP) neural network on an FPGA with varying data width. Reducing the data width from 8 to 4 bits only reduces prediction accuracy by 11%, but the FPGA area decreases by 41%. Compared to networks that use the sigmoid functions, our proposed D-ReLU function uses 24% - 41% less area with no loss to prediction accuracy. Further reducing the data width of the 3-layer networks from 8 to 4 bits, the prediction accuracies only decrease by 3% - 5%, with area being reduced by 9% - 28%. Moreover, FPGA solutions have 29 times faster execution time, even despite running at a 60× lower clock rate. Thus, FPGA implementations of neural networks offer a high-performance, low power alternative to traditional software methods, and our novel D-ReLU activation function offers additional improvements to performance and power saving.

Finite-time Mittag-Leffler synchronization of fractional-order delayed memristive neural networks with parameters uncertainty and discontinuous activation functions

The finite-time Mittag-Leffler synchronization is investigated for fractional-order delayed memristive neural networks(FDMNN)with parameters uncertainty and discontinuous activation functions.The relevant results are obtained under the framework of Filippov for such systems.Firstly,the novel feedback controller,which includes the discontinuous functions and time delays,is proposed to investigate such systems.Secondly,the conditions on finite-time Mittag-Leffler synchronization of FDMNN are established according to the properties of fractional-order calculus and inequality analysis technique.At the same time,the upper bound of the settling time for Mittag-Leffler synchronization is accurately estimated.In addition,by selecting the appropriate parameters of the designed controller and utilizing the comparison theorem for fractional-order systems,the global asymptotic synchronization is achieved as a corollary.Finally,a numerical example is given to indicate the correctness of the obtained conclusions.

Functional properties,antioxidant activity and in-vitro digestibility characteristics of brown and polished rice flours of Indian temperate region

The present investigation was aimed to study functional properties,antioxidant activity and in-vitro digestibility characteristics of brown and polished flours obtained from four rice cultivars(SR-4,K-39,Mushq Budij and Zhag)of Kashmir.Brown rice flours had higher total dietary fibre(3.08%-3.68%),oil absorption(116.0%-139.0%),emulsion capacity(4.78%-9.52%),emulsion stability(87.46%-99.93%)and resistant starch content(6.80%-9.00%)than polished flours.However,polished flours presented greater water absorption(102.0%-122.0%),foaming capacity(8.00%-13.63%),apparent amylose(19.16%-22.62%),peak(2260.0-2408.0 cP),trough(1372.0-1589.0 cP)and breakdown(714.0-978.0 cP)viscosities than their brown counterparts.Brown rice flours depicted highest total phenolic content(4.40-6.40 mg GAE/g)and inhibition of lipid peroxidation(19.50%-33.20%).However,equilibrium starch hydrolysis percentage(C∞)and predicted glycemic index of brown rice flours were lower than their polished counterparts.Among rice cultivars,brown Zhag flour had the highest total dietary fibre(3.68%),emulsion capacity(9.52%),emulsion stability(99.93%),resistant starch(9.00%),DPPH radical scavenging activity(85.45%)and inhibition of lipid peroxidation(33.20%),respectively.Emulsion capacity and emulsion stability were positively correlated with protein content of rice flours.However,peak,trough,breakdown and setback viscosities were negatively correlated with protein and fat contents of rice flour.The present investigation will be helpful in identifying nutritive role of rice flours from studied cultivars in human diet.

Effects of elafibranor on liver fibrosis and gut barrier function in a mouse model of alcohol-associated liver disease

BACKGROUND Alcohol-associated liver disease(ALD)is a leading cause of liver-related morbidity and mortality,but there are no therapeutic targets and modalities to prevent ALD-related liver fibrosis.Peroxisome proliferator activated receptor(PPAR)α and δ play a key role in lipid metabolism and intestinal barrier homeostasis,which are major contributors to the pathological progression of ALD.Meanwhile,elafibranor(EFN),which is a dual PPARαand PPARδagonist,has reached a phase III clinical trial for the treatment of metabolic dysfunctionassociated steatotic liver disease and primary biliary cholangitis.However,the benefits of EFN for ALD treatment is unknown.AIM To evaluate the inhibitory effects of EFN on liver fibrosis and gut-intestinal barrier dysfunction in an ALD mouse model.METHODS ALD-related liver fibrosis was induced in female C57BL/6J mice by feeding a 2.5% ethanol(EtOH)-containing Lieber-DeCarli liquid diet and intraperitoneally injecting carbon tetrachloride thrice weekly(1 mL/kg)for 8 weeks.EFN(3 and 10 mg/kg/day)was orally administered during the experimental period.Histological and molecular analyses were performed to assess the effect of EFN on steatohepatitis,fibrosis,and intestinal barrier integrity.The EFN effects on HepG2 lipotoxicity and Caco-2 barrier function were evaluated by cell-based assays.RESULTS The hepatic steatosis,apoptosis,and fibrosis in the ALD mice model were significantly attenuated by EFN treatment.EFN promoted lipolysis and β-oxidation and enhanced autophagic and antioxidant capacities in EtOH-stimulated HepG2 cells,primarily through PPARαactivation.Moreover,EFN inhibited the Kupffer cell-mediated inflammatory response,with blunted hepatic exposure to lipopolysaccharide(LPS)and toll like receptor 4(TLR4)/nuclear factor kappa B(NF-κB)signaling.EFN improved intestinal hyperpermeability by restoring tight junction proteins and autophagy and by inhibiting apoptosis and proinflammatory responses.The protective effect on intestinal barrier function in the EtOH-stimulated Caco-2 cells was predominantly mediated by PPARδ activation.CONCLUSION EFN reduced ALD-related fibrosis by inhibiting lipid accumulation and apoptosis,enhancing hepatocyte autophagic and antioxidant capacities,and suppressing LPS/TLR4/NF-κB-mediated inflammatory responses by restoring intestinal barrier function.

Changes in brain activation in stroke patients after mental practice and physical exercise: a functional MRI study

Mental practice is a new rehabilitation method that reters to the mental rehearsal ot motor imagery content with the goal of improving motor performance. However, the relationship between activated regions and motor recovery after mental practice training is not well understood. In this study, 15 patients who suffered a firstever subcortical stroke with neurological deficits affecting the right hand, but no significant cognitive impairment were recruited. 10 patients underwent mental practice combined with physical practice training, and 5 patients only underwent physical practice training. We observed brain activation regions after 4 weeks of training, and explored the correlation of activation changes with functional recovery of the affected hands. The results showed that, after 4 weeks of mental practice combined with physical training, the Fugl-Meyer assessment score for the affected right hand was significantly increased than that after 4 weeks of practice training alone. Functional MRI showed enhanced activation in the left primary somatosensory cortex, attenuated activation intensity in the right primary motor cortex, and enhanced right cerebellar activation observed during the motor imagery task using the affected right hand after mental practice training. The changes in brain cortical activity were related to functional recovery of the hand. Experimental findings indicate that cortical and cerebellar functional reorganization following mental practice contributed to the improvement of hand function.

Convolution-Based Heterogeneous Activation Facility for Effective Machine Learning of ECG Signals

Machine Learning(ML)and Deep Learning(DL)technologies are revolutionizing the medical domain,especially with Electrocardiogram(ECG),by providing new tools and techniques for diagnosing,treating,and preventing diseases.However,DL architectures are computationally more demanding.In recent years,researchers have focused on combining the computationally less intensive portion of the DL architectures with ML approaches,say for example,combining the convolutional layer blocks of Convolution Neural Networks(CNNs)into ML algorithms such as Extreme Gradient Boosting(XGBoost)and K-Nearest Neighbor(KNN)resulting in CNN-XGBoost and CNN-KNN,respectively.However,these approaches are homogenous in the sense that they use a fixed Activation Function(AFs)in the sequence of convolution and pooling layers,thereby limiting the ability to capture unique features.Since various AFs are readily available and each could capture unique features,we propose a Convolutionbased Heterogeneous Activation Facility(CHAF)which uses multiple AFs in the convolution layer blocks,one for each block,with a motivation of extracting features in a better manner to improve the accuracy.The proposed CHAF approach is validated on PTB and shown to outperform the homogeneous approaches such as CNN-KNN and CNN-XGBoost.For PTB dataset,proposed CHAF-KNN has an accuracy of 99.55%and an F1 score of 99.68%in just 0.008 s,outperforming the state-of-the-art CNN-XGBoost which has an accuracy of 99.38%and an F1 score of 99.32%in 1.23 s.To validate the generality of the proposed CHAF,experiments were repeated on MIT-BIH dataset,and the proposed CHAF-KNN is shown to outperform CNN-KNN and CNN-XGBoost.

Activation of brain areas following ankle dorsiflexion versus plantar flexion Functional magnetic resonance imaging verification

Changes in activated areas of the brain during ankle active dorsiflexion and ankle active plantar flexion were observed in six healthy subjects using functional magnetic resonance imaging. Excited areas of ankle active dorsiflexion involved the bilateral primary motor area and the primary somatosensory area, as well as the bilateral supplementary sensory area, the primary visual area, the right second visual area, and the vermis of cerebellum. Excited areas of ankle active plantar flexion included the ipsilateral supplementary motor area, the limbic system, and the contralateral corpus striatum. Fine movements of the cerebral cortex control the function of the ankle dorsiflexion to a larger extent than ankle plate flexion, and the function of ankle plate flexion is more controlled by the subcortical area.

Effects of C2 hemisection on respiratory and cardiovascular functions in rats

High ce rvical spinal co rd injuries induce permanent neuromotor and autonomic deficits.These injuries impact both central respiratory and cardiovascular functions through modulation of the sympathetic nervous system.So far,cardiovascular studies have focused on models of complete contusion or transection at the lower cervical and thoracic levels and diaphragm activity evaluations using invasive methods.The present study aimed to evaluate the impact of C2 hemisection on different parameters representing vital functions(i.e.,respiratory function,cardiovascular,and renal filtration parameters)at the moment of injury and 7 days post-injury in rats.No ventilatory parameters evaluated by plethys mography were impacted during quiet breathing after 7 days post-injury,whereas permanent diaphragm hemiplegia was observed by ultrasound and confirmed by diaphragmatic electromyography in anesthetized rats.Interestingly,the mean arterial pressure was reduced immediately after C2 hemisection,with complete compensation at 7 days post-injury.Renal filtration was unaffected at 7 days post-injury;however,remnant systolic dysfunction chara cterized by a reduced left ventricular ejection fraction persisted at 7 days post-injury.Taken together,these results demonstrated that following C2 hemisection,diaphragm activity and systolic function are impa cted up to 7 days post-injury,whereas the respiratory and cardiovascular systems display vast ada ptation to maintain ventilatory parameters and blood pressure homeostasis,with the latter likely sustained by the remaining descending sympathetic inputs spared by the initial injury.A better broad characterization of the physiopathology of high cervical spinal cord injuries covering a longer time period post-injury could be beneficial for understanding evaluations of putative therapeutics to further increase cardiorespiratory recovery.

Aging effects of regional activation in a spatial task A functional magnetic resonance imaging study

BACKGROUND： An increasing number of studies have shown the effects of aging in basic cognitive processing and higher cognitive functions using functional magnetic resonance imaging （fMRI）. However, little is known about the aging effects in diverse cognitive abilities, such as spatial learning and reasoning. OBJECTIVE： To investigate the effect of aging on spatial cognitive performance and regional brain activation based on fMRI. DESIGN, TIME, AND SETTING： A block design for fMRI observation. This study was performed at the fMRI Laboratory, Brain Science Research Center, Korea Advanced Institute of Science and Technology from March 2006 to May 2009. PARTICIPANTS： Eight right-handed, male, college students in their 20s （mean age 21.5 years） and six right-handed, male, adults in their 40s （mean age 45.7 years）, who graduated from college, participated in the study. All subjects were healthy and had no prior history of psychiatric or neurological disorders. METHODS： A spatial task was presented while brain images were acquired using a 3T fMRI system （ISOL Technology, Korea）. The spatial tasks involved selecting a shape that corresponded to a given figure using four examples, as well as selecting a development figure of a diagram. MAIN OUTCOME MEASURES： The accuracy rate （number of correct answers/total number of items x 100%） of spatial tasks was calculated. Using the subtraction procedure, the activated areas in the brain during spatial tasks were color-coded by T-score. The double subtraction method was used to analyze the effect of aging between the two age groups （20s versus 40s）. RESULTS： The cerebellum, occipital lobe, parietal lobe, and frontal lobe were similarly activated in the two age groups. Increased brain activations, however, were observed in bilateral parietal and superior frontal lobes of the younger group. More activation was observed in bilateral middle frontal and right inferior frontal lobes in the older group. Compared with the older group, the younger men exhibited greater spatial performance （P = 0.012）. CONCLUSION： Reduced cognitive function correlated with decreased activation areas in the parietal lobe and altered activation in the frontal lobe.