Decreased segregation of brain systems across the healthy adult lifespan

Healthy aging has been associated with decreased specialization in brain function. This characterization has focused largely on describing age-accompanied differences in specialization at the level of neurons and brain areas. We expand this work to describe systems-level differences in specialization in a healthy adult lifespan sample (n = 210; 20–89 y). A graph-theoretic framework is used to guide analysis of functional MRI resting-state data and describe systems-level differences in connectivity of individual brain networks. Young adults’ brain systems exhibit a balance of within- and between-system correlations that is characteristic of segregated and specialized organization. Increasing age is accompanied by decreasing segregation of brain systems. Compared with systems involved in the processing of sensory input and motor output, systems mediating “associative” operations exhibit a distinct pattern of reductions in segregation across the adult lifespan. Of particular importance, the magnitude of association system segregation is predictive of long-term memory function, independent of an individual’s age.Healthy adult aging is characterized by a progressive degradation of brain structure and function associated with gradual changes in cognition (see reviews in refs. 1, 2). Among the age-accompanied functional changes, one prominent observation is a reduction in the specificity with which distinct neural structures mediate particular processing roles [i.e., a reduction in functional specialization, or “dedifferentiation” (3)]. A reduction in functional specificity has been observed across multiple spatial scales of brain organization, ranging from the firing patterns of single neurons (e.g., refs. 4, 5) to the evoked activity of individual brain areas (6–10). (For additional discussion see ref. 11.)Despite the compelling evidence for age-accompanied reductions in functional specialization across numerous brain areas, the relationship between neural specialization and cognition generally is weak. This likely is related to the fact that broad cognitive domains such as “long-term memory” and “executive control” are mediated by distributed and interacting brain systems, each consisting of multiple interacting brain areas. Thus, relating functional specialization in a single brain area to general measures of cognition likely will be unsuccessful. Such an argument is consistent with the view that severe impairment in cognitive function due to injury or insult typically is a consequence of damage to multiple brain locations (e.g., refs. 12, 13). Based on these considerations, it seems plausible that the cognitive decline evident even in healthy older adults may be related to decreased functional integrity at a systems level of organization. The present report approaches healthy aging from this systems-level perspective in an effort to relate systems-related functional specialization to age-accompanied differences in cognition.Before proceeding, it is important to clarify the meaning of system. The term “system” often is used in relation to brain organization when referring to any group of areas that subserve common processing roles. For example, the visual system comprises brain areas primarily defined by their role in processing visual stimuli (e.g., ref. 14), and the frontal–parietal task control system consists of brain areas involved mainly in adaptive task control (15). Identifying distinct brain systems and defining their functional roles by examining how their constituent areas are modulated by experimental testing or are impaired by brain damage is not an easy endeavor; systems of brain areas typically mediate processing roles that span multiple stimulus and task demands. This reality makes assessing changes in the functional specialization of systems across cohorts of individuals extremely challenging.An alternative formal and complementary approach to defining a brain system involves understanding how brain areas relate to one another via their patterns of shared functional or anatomical relationships in the context of a large-scale network (16, 17). Like many other complex networks, brain networks may be analyzed as a graph of connected or interacting elements. When a brain network graph represents the interaction of areas, one prominent feature is the presence of subsets of areas that are highly interactive with one another and less interactive with other subsets of areas. This pattern of organization reflects the presence of distinct “modules” or “communities” (e.g., ref. 18). Importantly, numerous connectivity-defined human brain modules have been shown to overlap closely with functional systems as defined by other methods of assessing information processing [e.g., task-evoked activity, lesion-mapping (19, 20)]. The close correspondence between differing methods of system identification provides a basis for using connectivity to understand the organization of brain systems and how they may differ with age.Modular brain networks are characterized by a fine balance of dense within-system relationships among brain areas (nodes) that have highly related processing roles, as well as sparser (but not necessarily absent) relationships between areas in systems with divergent processing roles. This pattern of system segregation facilitates communication among brain areas that may be distributed anatomically but nevertheless are in the service of related sets of processing operations, and simultaneously reinforces the functional specialization of systems that perform different sets of processing operations (21). Importantly, segregated systems can communicate with one another via the presence of the sparser connections between them. As such, any deviation in the patterns of within- and between-system connectivity may be considered evidence for a change in the system’s specialization. Furthermore, if aging is associated with changes in functional specialization at the level of brain systems, this may be revealed by examining the differences in patterns of within- and between-system areal connectivity across age.We use functional connectivity, as measured by blood oxygen-level–dependent (BOLD) functional MRI (fMRI) during rest [resting-state functional correlations (RSFCs), see ref. 22], to assess age-related differences in the organization of brain systems. Changes in RSFC patterns between sets of areas have been observed following extensive directed training (23–25), and differences in RSFC patterns also have been reported in cross-sectional comparisons spanning from childhood to older age (e.g., refs. 26–29). The extant data suggest that RSFCs are malleable and reflect sensitivity to a history of coactivation: changes in the processing roles of areas may be characterized by changes in their RSFCs with other areas (for discussion, see ref. 17). This feature makes RSFCs particularly useful in assessing differences in the organization and specialization of brain systems.In the present study, the age-accompanied differences in the functional specialization of brain systems are revealed by examining patterns of within- and between-system areal RSFCs in a large healthy adult lifespan sample (n = 210; age range, 20–89 y). The inclusion of subjects distributed across each decade of adulthood not only allows us to assess how older and younger adults differ in their organization of brain systems, but also provides insight as to whether there is a critical stage of the adult lifespan when differences in system organization may appear. Previous reports attempted to address related questions by examining end points of the adult aging spectrum, focusing on the organization within specific systems (e.g., refs. 26, 28, 30), or using area nodes that are not representative of functional areas [e.g., structural parcels (31–34)]. The latter feature likely contributes to the inconsistent findings observed in the relationship between summary network measures and age groups (e.g., refs. 31, 35 vs. refs. 30, 36). In addition to examining age-related differences in system organization developed from a biologically plausible cortical parcellation of the human brain network, we also relate systems-level differences in organization to differences in general measures of cognitive ability. To foreshadow the results that follow, we report that aging is associated with differences in patterns of connectivity within and between brain systems, that these differences are not uniform across all systems, and that the segregation of brain systems has a direct relationship to measures of cognitive ability independent of age.     

Design,synthesis and pharmacological analysis of 5-[4′-(substituted-methyl)[1,1′-biphenyl]-2-yl]-1<Emphasis Type="Italic">H</Emphasis>-tetrazoles

In the present paper 5-[4′-({4-[(4-aryloxy)methyl]-1H-1,2,3-triazol-1-yl}methyl)[1,1′-biphenyl]-2-yl]-1H-tetrazoles (5a–g) and [2′-(1H-tetrazol-5-yl)[1,1′-biphenyl]-4-yl]methyl-substituted-1-carbodithioates (11h–q) have been designed and synthesized. These compounds were subjected to docking (against AT₁ receptor protein enzyme in complex with Lisinopril), in vitro angiotensin converting enzyme inhibition, anti-proliferative, anti-inflammatory screening (through egg albumin denaturation inhibition and red blood cell membrane stabilization assay) and finally anti-fungal activity analyses. Some of the compounds have shown significant pharmacological properties.     

Obstructive Sleep Apnea: Role of an Otorhinolaryngologist

Obstructive sleep apnea is a disorder resulting from collapse of the upper airway during sleep. Its etiology is multifactorial, resulting from the interdependence of structurally vulnerable upper airway anatomy interacting with physiologic mechanism of ventilator instability during sleep. The ENT causes for OSA are relatively simple conditions that can be treated by safe and simple medical and/or surgical procedures. To assess the prevalence of ENT disorders in patients presenting to the sleep clinic. Patients presented to sleep clinic were submitted to an assessment protocol including clinical history, otorhinolaryngology examination and a polysomnography. Total 69 patients were included and distributed into two groups according to AHI: patients with sleep disordered breathing only (simple snorer and/or AHI ≤ 5) and patients with obstructive sleep apnea syndrome (AHI > 5). There was significant statistical difference for deviated nasal septum (p = 0.0004) and inferior turbinate hypertrophy (p = 0.03) in both groups. Most patients were in the class III and IV of Mallampati classification. Odds of having OSA increases more than 1.5 folds as the level of Mallampati classification increases by one class. ENT disorders were more common in the patients with OSA than in simple snorers and have impact on pathophysiology of OSA and its treatment modality. Hence, ENT examination in all patients with sleep disordered breathing will be helpful.     

Human Bioequivalence Evaluation of Two Losartan Potassium Tablets Under Fasting Conditions

The bioequivalence of two different tablet formulations containing losartan potassium 100 mg was determined in healthy volunteers after a single oral dose in a randomized crossover study. Test and reference products were administered to 60 volunteers with 240 ml water after overnight fasting. Plasma concentrations of losartan and its active carboxylic acid metabolite were monitored over a period of 36 h after drug administration by validated LC/MS/MS analytical method. The pharmacokinetic parameters C_max, AUC_0-t, AUC_0-∞, AUC_0-t/AUC_0-∞, t_max, K_el and t_½ were determined from plasma concentration time profile of both formulations for losartan and its active metabolite losartan carboxylic acid and were found to be in good agreement. The carboxylic acid metabolite was considered for profiling purpose only. The analysis of variance did not show any significant difference between the two formulations and 90% confidence intervals for the ratio of C_max (84.89-104.09%), AUC_0-t (95.84-102.84%) and AUC_0-∞ (96.43-103.25%) values for losartan between the test and reference products were within the 80-125% interval, satisfying the bioequivalence criteria of the US FDA guidelines. These results indicate that the test and the reference products of losartan potassium are bioequivalent and, thus, may be prescribed interchangeably.     

Pyridine-catalyzed synthesis of quinoxalines as anticancer and anti-tubercular agents

Quinoxaline derivatized with coumarin viz., 3a–f and with sydnones viz., 7a–0 were synthesized using pyridine as catalyst. Among the coumarin derivatives, 3a and 3b have been screened for anticancer activity against 60 human cancer cell lines at NIH (USA). Compound 3a has shown 55.75 % GI against Melanoma (MALME-M) tumor cell line. Further, the sydnone derivatives 7d–i inhibited the Mycobacterium tuberculae H₃₇RV.     

Bioanalytical Method for Carbocisteine in Human Plasma by Using LC-MS/MS: A Pharmacokinetic Application

A simple, sensitive, and selective LC-MS/MS method was developed and validated for the quantification of carbocisteine in human plasma. Rosiglitazone was used as the internal standard and heparin was used as the anticoagulant. The chromatographic separation was performed by using the Waters Symmetry Shield RP 8, 150 × 3.9 mm, 5 μ column at 40°C with a mobile phase consisting of a mixture of methanol and 0.5% formic acid solution in a 40:60 proportion. The flow rate was 500 μl/min along with a 5 μl injection volume. Protein precipitation was used as the extraction method. Mass spectrometric data were detected in positive ion mode. The MRM mode of the ions for carbocisteine was 180.0 > 89.0 and for rosiglitazone it was 238.1 > 135.1. The method was validated in the concentration curve range of 50.000 ng/mL to 6000.000 ng/mL. The retention times of carbocisteine and the internal standard rosiglitazone were approximately 2.20 and 3.01 min, respectively. The overall run time was 4.50 min. This method was found suitable to analyze human plasma samples for the application in pharmacokinetic and BA/BE studies.