Minimum spanning tree reflects the alterations of the default mode network during Alzheimer's disease.

This study analyzes the connectivity pattern of the default mode network (DMN) in patients with Alzheimer's disease (AD) in comparison with young and elderly controls using the minimum spanning tree (MST). This tree is a tool from graph theory and connects all the nodes of a graph with the minimum cost. The findings revealed that the alterations of the basic structure represented by the MST might provide valuable insights about the physiopathology of the disease. Additionally, by making use of the MST for functionally clustering the DMN, it was shown that the functional subnetworks comprising the DMN differed among the three subject groups. Nonetheless, there were intact prefrontal and temporal networks in elderly controls and AD patients, as well. The analysis shows that although the topologies of the MST characterized by the degree distributions do not differ significantly among the groups, the DMN of the AD patients exhibits a higher segregation, insomuch that posterior cingulate/precuneus and hippocampus/parahippocampus are heavily isolated from rest of the network. We conclude that the MST can be used effectively for analyzing cortical networks.

Association among SNAP-25 gene DdeI and MnlI polymorphisms and hemodynamic changes during methylphenidate use: a functional near-infrared spectroscopy study.

OBJECTIVE: To investigate the interaction of treatment-related hemodynamic changes with genotype status for Synaptosomal associated protein 25 (SNAP-25) gene in participants with attention deficit hyperactivity disorder (ADHD) on and off single dose short-acting methylphenidate treatment with functional near-infrared spectroscopy (fNIRS). METHOD: A total of 15 right-handed adults and 16 right-handed children with DSM-IV diagnosis of ADHD were evaluated. Ten milligrams of short-acting methylphenidate was administered in a crossover design. RESULTS: Participants with SNAP-25 DdeI T/T genotype had decreased right deoxyhemoglobin ([HHb]) with treatment. SNAP-25 MnlI genotype was also associated with right deoxyhemoglobin ([HbO2]) and [HHb] changes as well as left [HHb] change. When the combinations of these genotypes were taken into account, the participants with [DdeI C/C or T/C and MnlI G/G or T/G] genotype had increased right [HHb] with MPH use whereas the participants with [DdeI T/T and MnlI T/T] or [DdeI T/T and MnlI G/G or T/G] genotypes had decreased right prefrontal [HHb]. CONCLUSIONS: These results suggested that SNAP-25 polymorphism might be associated with methylphenidate induced brain hemodynamic changes in ADHD participants.

On temporal connectivity of PFC via Gauss-Markov modeling of fNIRS signals.

Functional near-infrared spectroscopy (fNIRS) is an optical imaging method, which monitors the brain activation by measuring the successive changes in the concentration of oxy- and deoxyhemoglobin in real time. In this study, we present a method to investigate the functional connectivity of prefrontal cortex (PFC) Sby applying a Gauss-Markov model to fNIRS signals. The hemodynamic changes on PFC during the performance of cognitive paradigm are measured by fNIRS for 17 healthy adults. The color-word matching Stroop task is performed to activate 16 different regions of PFC. There are three different types of stimuli in this task, which can be listed as incongruent stimulus (IS), congruent stimulus (CS), and neutral stimulus (NS), respectively. We introduce a new measure, called "information transfer metric" (ITM) for each time sample. The behavior of ITMs during IS are significantly different from the ITMs during CS and NS, which is consistent with the outcome of the previous research, which concentrated on fNIRS signal analysis via color-word matching Stroop task. Our analysis shows that the functional connectivity of PFC is highly relevant with the cognitive load, i.e., functional connectivity increases with the increasing cognitive load.

Multilevel statistical inference from functional near-infrared spectroscopy data during stroop interference.

Functional near-infrared spectroscopy (fNIRS) is an emerging technique for monitoring the concentration changes of oxy- and deoxy-hemoglobin (oxy-Hb and deoxy-Hb) in the brain. An important consideration in fNIRS-based neuroimaging modality is to conduct group-level analysis from a set of time series measured from a group of subjects. We investigate the feasibility of multilevel statistical inference for fNIRS. As a case study, we search for hemodynamic activations in the prefrontal cortex during Stroop interference. Hierarchical general linear model (GLM) is used for making this multilevel analysis. Activation patterns both at the subject and group level are investigated on a comparative basis using various classical and Bayesian inference methods. All methods showed consistent left lateral prefrontal cortex activation for oxy-Hb during interference condition, while the effects were much less pronounced for deoxy-Hb. Our analysis showed that mixed effects or Bayesian models are more convenient for faithful analysis of fNIRS data. We arrived at two important conclusions. First, fNIRS has the capability to identify activations at the group level, and second, the mixed effects or Bayesian model is the appropriate mechanism to pass from subject to group-level inference.

Constraining the general linear model for sensible hemodynamic response function waveforms.

We propose a method to do constrained parameter estimation and inference from neuroimaging data using general linear model (GLM). Constrained approach precludes unrealistic hemodynamic response function (HRF) estimates to appear at the outcome of the GLM analysis. The permissible ranges of waveform parameters were determined from the study of a repertoire of plausible waveforms. These parameter intervals played the role of prior distributions in the subsequent Bayesian analysis of the GLM, and Gibbs sampling was used to derive posterior distributions. The method was applied to artificial null data and near infrared spectroscopy (NIRS) data. The results show that constraining the GLM eliminates unrealistic HRF waveforms and decreases false activations, without affecting the inference for "realistic" activations, which satisfy the constraints.

Respiratory airflow estimation by time varying autoregressive modeling.

This study proposes an approach to estimate airflow from respiratory sounds using time varying autoregressive modeling. Exploiting the well-established relationship between respiratory sounds and airflow, the sound signals from posterior chest and trachea are modeled using TVAR with a Fourier basis set. It is hypothesized that TVAR coefficients will give us an estimate of the amount of respiratory airflow. The procedure was tested on a group of healthy subjects and good correlation coefficients between the estimated and actual airflow were obtained.

Quantitative proton MR spectroscopic findings of cortical reorganization in the auditory cortex of musicians.

BACKGROUND AND PURPOSE: Brain has a capacity for reorganization that enables use-dependent adaptations to acquire skills. Previous studies demonstrated morphometric and functional use-dependent changes in the brains of musicians. The purpose of this study was to investigate the differences in metabolite concentrations in the planum temporale, an area strongly associated with the processing of music perception, between trained musicians and non-musicians. We hypothesized that the microscopic changes leading to use-dependent adaptations in brain might cause neurometabolite changes that could be detected with quantitative proton MR spectroscopy. METHODS: We performed quantitative proton MR spectroscopy in the left planum temporale of 10 musicians (six men and four women; age range, 20-37 years) and in those of 10 age- and sex-matched control subjects who had no musical training. We calculated the major metabolite concentrations in the left planum temporale. RESULTS: The difference in N-acetylaspartate (NAA) concentrations between the musicians and the non-musician control subjects was statistically significant (P <.01). No significant difference was noted in the choline and creatine concentrations between the musicians and the non-musician control subjects (P >.05). The NAA concentration of the musicians correlated with the total duration of musical training and activity (r=0.733, P <.05). CONCLUSION: Long-term, professional musical activity caused significant changes in the neurometabolite concentrations that might reflect the physiologic mechanism(s) of use-dependent adaptation in the brains of musicians.