Detection of functional connectivity using temporal correlations in MR images

Functional connectivity among brain regions has been investigated via an analysis of correlations between regional signal fluctuations recorded in magnetic resonance (MR) images obtained in a steady state. In comparison with studies of functional connectivity that utilize task manipulations, the analysis of correlations in steady state data is less susceptible to confounds arising when functionally unrelated brain regions respond in similar ways to changes in task. A new approach to identifying interregional correlations in steady state data makes use of two independent data sets. Regions of interest (ROIs) are defined and hypotheses regarding their connectivity are generated in one data set. The connectivity hypotheses are then evaluated in the remaining (independent) data set by analyzing low frequency temporal correlations between regions. The roles of the two data sets are then reversed and the process repeated, perhaps multiple times. This method was illustrated by application to the language system. The existence of a functional connection between Broca's area and Wernicke's area was confirmed in healthy subjects at rest. An increase in this functional connection when the language system was actively engaged (when subjects were continuously listening to narrative text) was also confirmed. In a second iteration of analyses, a correlation between Broca's area and a region in left premotor cortex was found to be significant at rest and to increase during continuous listening. These findings suggest that the proposed methodology can reveal the presence and strength of functional connections in high-level cognitive systems.     

Outcome after cardiac arrest during acute myocardial infarction

A community-wide study of acute myocardial infarction (AMI) was conducted in all 16 acute-care general hospitals in the Worcester, Massachusetts, metropolitan area during the years 1975, 1978, 1981 and 1984. The in-hospital and long-term prognoses of 667 patients with AMI complicated by cardiac arrest (CA) was compared with that of 2,596 AMI patients without CA. The incidence of CA complicating AMI was similar (21%) during each of the 4 study years. Among patients with AMI who had CA, 36% had CA within the first day of hospitalization and 48% within the first 2 days. The in-hospital case-fatality rate was much higher for AMI patients with CA (78%) than for those without CA (4%) (p less than 0.001). For patients discharged alive from the hospital, a trend toward a higher mortality rate was seen at 1 and 2 years after hospital discharge for patients with CA; however, long-term survival rates were not significantly different between AMI patients with and without CA. When time of occurrence of CA relative to in-hospital survival was examined, patients with early CA (within 1 day or within 2 days of hospital admission) had a significantly greater in-hospital survival (39% and 34%) than did those with late CA (after 1 day or after 2 days) (13% and 12%). Similarly, patients discharged from the hospital after early CA had a significantly better chance of long-term survival than patients discharged after late CA.     

Whole-exome sequencing identifies ALMS1, IQCB1, CNGA3, and MYO7A mutations in patients with Leber congenital amaurosis

It has been well documented that mutations in the same retinal disease gene can result in different clinical phenotypes due to difference in the mutant allele and/or genetic background. To evaluate this, a set of consanguineous patient families with Leber congenital amaurosis (LCA) that do not carry mutations in known LCA disease genes was characterized through homozygosity mapping followed by targeted exon/whole-exome sequencing to identify genetic variations. Among these families, a total of five putative disease-causing mutations, including four novel alleles, were found for six families. These five mutations are located in four genes, ALMS1, IQCB1, CNGA3, and MYO7A. Therefore, in our LCA collection from Saudi Arabia, three of the 37 unassigned families carry mutations in retinal disease genes ALMS1, CNGA3, and MYO7A, which have not been previously associated with LCA, and 3 of the 37 carry novel mutations in IQCB1, which has been recently associated with LCA. Together with other reports, our results emphasize that the molecular heterogeneity underlying LCA, and likely other retinal diseases, may be highly complex. Thus, to obtain accurate diagnosis and gain a complete picture of the disease, it is essential to sequence a larger set of retinal disease genes and combine the clinical phenotype with molecular diagnosis.     

Functional connectivity‐based parcellation of amygdala using self‐organized mapping: A data driven approach

The overall goal of this work is to demonstrate how resting state functional magnetic resonance imaging (fMRI) signals may be used to objectively parcellate functionally heterogeneous subregions of the human amygdala into structures characterized by similar patterns of functional connectivity. We hypothesize that similarity of functional connectivity of subregions with other parts of the brain can be a potential basis to segment and cluster voxels using data driven approaches. In this work, self‐organizing map (SOM) was implemented to cluster the connectivity maps associated with each voxel of the human amygdala, thereby defining distinct subregions. The functional separation was optimized by evaluating the overall differences in functional connectivity between the subregions at group level. Analysis of 25 resting state fMRI data sets suggests that SOM can successfully identify functionally independent nuclei based on differences in their inter subregional functional connectivity, evaluated statistically at various confidence levels. Although amygdala contains several nuclei whose distinct roles are implicated in various functions, our objective approach discerns at least two functionally distinct volumes comparable to previous parcellation results obtained using probabilistic tractography and cytoarchitectonic analysis. Association of these nuclei with various known functions and a quantitative evaluation of their differences in overall functional connectivity with lateral orbital frontal cortex and temporal pole confirms the functional diversity of amygdala. The data driven approach adopted here may be used as a powerful indicator of structure–function relationships in the amygdala and other functionally heterogeneous structures as well. Hum Brain Mapp 35:1247–1260, 2014. © 2013 Wiley Periodicals, Inc.     

Cross hippocampal influence in mesial temporal lobe epilepsy measured with high temporal resolution functional magnetic resonance imaging

Purpose Mesial temporal lobe epilepsy (mTLE) is a chronic disorder with spontaneous seizures recurring for years, or even decades. Many structural and functional changes have been detected in both the seizure focus and distal regions throughout the brain over this duration that may reflect the development of epileptogenic networks. Resting state functional magnetic resonance imaging (fMRI) connectivity mapping has the potential to elucidate and quantify these networks. The network between the left and right hippocampus may very likely be one of the most susceptible to changes due to long‐term seizure propagation effects. Therefore, the objective of this study was to quantify cross hippocampal influence in mTLE using high temporal resolution fMRI, and to determine its relationship with disease duration. Methods fMRI images were acquired in the resting (interictal) state with 500 ms temporal resolution across the temporal lobes of 19 mTLE patients (13 left, 6 right). The left and right hippocampi were identified on each subject’s images using both structurally defined and functionally defined boundaries. The cross hippocampal influence was quantified in two ways for each pair of regions: (1) the nondirectional hippocampal functional connectivity calculated as the Pearson’s correlation between the average time series in the left and the right hippocampus regions, and (2) the Granger causality (GC) laterality measure, which implies directional influence by determining temporal precedence. Each of these measures was correlated with age, age of onset, and disease duration across subjects to investigate relationship to disease progression. Key Findings The hippocampal connectivity was not significantly different between patients with left and right mTLE using either the structurally or the functionally defined regions. Across all patients, hippocampal connectivity was not correlated significantly with age of onset or duration of disease. However, as duration of disease increased after 10 years (nine patients), the hippocampal connectivity increased linearly. Using the functionally defined regions, the GC laterality was increased in the right mTLE over the left mTLE, indicating that the left hippocampus was influencing the right hippocampus more than the right influencing left. This was also positively correlated with age of onset. Furthermore, like hippocampal connectivity, the relationship between GC laterality and duration of disease changes after 10 years duration of disease. After this duration, the GC laterality was positive in the three of three patients with right mTLE (left influencing right), whereas the GC laterality was negative in five of six patients with left mTLE (right influencing left). Significance This study reveals a relationship between fMRI functional connectivity and causal influence of the left and right hippocampi and duration of disease in mTLE. During the interictal state, the interhemispheric hippocampal connectivity initially is disrupted and then linearly increases as the epilepsy progresses longer than 10 years. This increase in connectivity appears to be due to the hippocampus contralateral to the epileptogenic focus exerting more influence over the ipsilateral hippocampus. These findings may have implications in understanding the functional development of epileptic networks and possibly prediction of surgical outcome of mTLE.     

Selecting an Appropriate Medication for Treating Neuropathic Pain in Patients with Diabetes: A Study Using the U.K. and Germany Mediplus Databases

Objective:   To evaluate the appropriateness of prescribing select neuropathic pain medications to diabetes patients based on the potential for drug–drug interactions with medications diabetes patients were prescribed continuously for ≥ 3 months (chronic use).
Methods:   Medical records of patients with a diagnosis of diabetes or use of antidiabetic medications between January 1, 2002 and September 30, 2005 in the U.K. and Germany Mediplus databases were obtained.
Patients:   Medication use profiles were evaluated between April 2004 and September 2005. The metabolic pathways associated with medications that were prescribed chronically to at least 10% of study patients were compared with the metabolic pathways of neuropathic pain medications to identify potential drug–drug interactions.
Results:   A total of 40,448 patients in the U.K. (63.6 ± 16.6 years, 51% male) and 31,930 patients in Germany (68.9 ± 12.7 years, 46% male) were identified. Frequently prescribed medications in the U.K. included aspirin (33.7%), metformin (32.7%), simvastatin (25.5%), atorvastatin (19.4%), atenolol (18.1%), and in Germany hydrochlorothiazide (35.8%), aspirin (25.2%), metformin (21.6%), metoprolol (20.3%), and simvastatin (18.3%). Several neuropathic pain medications have potential for drug–drug interactions with medications prescribed to diabetes patients. Examples include (neuropathic pain medications vs. diabetes medications): duloxetine, paroxetine, and methadone (CYP2D6 inhibitors) and oxycodone HCL, hydrocodone (CYP2D6 substrates) vs. metoprolol and bisoprolol (CYP2D6 substrates); and carbamazepine (CYP3A4 inducer) vs. simvastatin, and atorvastatin (CYP3A4 substrates).
Conclusions/Interpretation:   Our findings underscore the need for medical vigilance when selecting medications for treating neuropathic pain in diabetes patients. ▪