The August 2015 issue of Minnesota Medicine includes an article by Carmen Peota about BSC researcher Dr. Lisa James and her three-year study of post-traumatic stress disorder (PTSD) and female veterans. "A Focus On Female Vets" describes Dr. James' interest in "defining the neural signature of PTSD for women and finding a genetic basis for why some women might be more resilient than others."
Margaret (Maggie) Mahan, Chelley Chorn and Apostolos Georgopoulos have published "White Noise Test: detecting autocorrelation and nonstationarities in long time series after ARIMA modeling" in the Proceedings of the 14th Python in Science Conference (SciPy 2015).
"In this paper, we presented an expansion on the Box-Jenkins methodology to ARIMA modeling ... Overall, using our approach, an investigator can perform ARIMA modeling and evaluate candidate models with ease for large datasets and datasets containing long time series." (Mahan et al., 2015)
Time series analysis has been a dominant technique for assessing relations within datasets collected over time and is becoming increasingly prevalent in the scientific community; for example, assessing brain networks by calculating pairwise correlations of time series generated from different areas of the brain. The assessment of these relations relies, in turn, on the proper calculation of interactions between time series, which is achieved by rendering each individual series stationary and nonautocorrelated (i.e., white noise, or to "prewhiten" the series). This ensures that the relations computed subsequently are due to the interactions between the series and do not reflect internal dependencies of the series themselves.
An established method for prewhitening time series is to apply an Autoregressive (AR, p) Integrative (I, d) Moving Average (MA, q) model (ARIMA) and retain the residuals. To diagnostically check whether the model orders (p, d, q) are sufficient, both visualization and statistical tests (e.g., Ljung-Box test) of the residuals are performed. However, these tests are not robust for high-order models in long time series. Additionally, as dataset size increases (i.e., number of time series to model) it is not feasible to visually inspect each series independently. As a result, there is a need for robust alternatives to diagnostic evaluations of ARIMA modeling.
Here, we demonstrate how to perform ARIMA modeling of long time series using Statsmodels, a library for statistical analysis in Python. Then, we present a comprehensive procedure (White Noise Test) to detect autocorrelation and nonstationarities in prewhitened time series, thereby establishing that the series does not differ significantly from white noise. This test was validated using time series collected from magnetoencephalography recordings. Overall, our White Noise Test provides a robust alternative to diagnostic checks of ARIMA modeling for long time series.
BSC researcher Dr. Sofia Sakellaridi has a paper appearing in the August 2015 issue of Experimental Brain Research entitled, "Neural mechanisms underlying the exploration of small city maps using magnetoencephalography". Along with her colleagues Peka Christova, Vassilios Christopoulos, Arthur C. Leuthold, John Peponis and Apostolos P. Georgopoulos, Dr. Sakellaridi "conducted a novel brain imaging experiment to test the hypothesis that a network of cortical regions is involved in the processing of spatial information acquired during exploration to make a decision. We recruited 10 subjects and asked them to explore small city maps exemplifying five different street network types (i.e., regular, colliding, curvilinear, cul-de-sac, and supergrid) to build a hypothetical City Hall, while neuronal activity was recorded continuously by 248 MEG sensors at high temporal resolution."
The neural mechanisms underlying spatial cognition in the context of exploring realistic city maps are unknown. We conducted a novel brain imaging study to address the question of whether and how features of special importance for map exploration are encoded in the brain to make a spatial decision. Subjects explored by eyes small city maps exemplifying five different street network types in order to locate a hypothetical City Hall, while neural activity was recorded continuously by 248 magnetoencephalography (MEG) sensors at high temporal resolution. Monitoring subjects' eye positions, we locally characterized the maps by computing three spatial parameters of the areas that were explored. We computed the number of street intersections, the total street length, and the regularity index in the circular areas of 6 degrees of visual angle radius centered on instantaneous eye positions.
We tested the hypothesis that neural activity during exploration is associated with the spatial parameters and modulated by street network type. All time series were rendered stationary and nonautocorrelated by applying an autoregressive integrated moving average model and taking the residuals. We then assessed the associations between the prewhitened time-varying MEG time series from 248 sensors and the prewhitened spatial parameters time series, for each street network type, using multiple linear regression analyses.
In accord with our hypothesis, ongoing neural activity was strongly associated with the spatial parameters through localized and distributed networks, and neural processing of these parameters depended on the type of street network. Overall, processing of the spatial parameters seems to predominantly involve right frontal and prefrontal areas, but not for all street network layouts. These results are in line with findings from a series of previous studies showing that frontal and prefrontal areas are involved in the processing of spatial information and decision making. Modulation of neural processing of the spatial parameters by street network type suggests that some street network layouts may contain other types of spatial information that subjects use to explore maps and make spatial decisions.
BSC investigators Vassilios Christopoulos, Angeliki Georgopoulos and Apostolos P. Georgopoulos have published their paper entitled,"The effect of apolipoprotein E4 on synchronous neural interactions in brain cultures" in the April 2015 issue of Experimental Brain Research. To their knowledge, this is the first study of the effects of apoE4 on neural network function in vitro. ApoE4 has been associated with various aspects of brain function and disease. The mechanisms of action of apoE4 in the brain are only partially understood and encompass various levels of reference. At the gross disease level, apoE4 is a known risk for Alzheimer’s disease, is involved in early onset of Alzheimer’s disease neuropathology in Down’s syndrome, adversely affects the sequelae of traumatic brain injury, affects susceptibility, clinical type and progression rate in multiple sclerosis, and is associated with higher symptom severity in posttraumatic stress disorder (PTSD).
Apostolos P Georgopoulos
This summer, the Brain Sciences Center will embark on a 3-year research project entitled, "Gulf War Illness as a Brain Autoimmune Disorder". In order to to establish a rationale for diagnosis and treatment for vets with GWI, investigators will study its commonalities with multiple sclerosis, Sjogren's syndrome and posttraumatic stress disorder (PTSD).
"Neural communication in posttraumatic growth" is the most recent publication by BSC researchers Samantha L. Anders, Carly K. Peterson, Lisa M. James, Brian E. Engdahl, Arthur Leuthold and Apostolos P. Georgopoulos. In this magnetoencephalography (MEG) study, they looked at posttraumatic growth (PTG), or positive psychological changes following exposure to traumatic events. The article is featured in the April 17 2015 issue of Experimental Brain Research.
Posttraumatic growth (PTG), or positive psychological changes following exposure to traumatic events, is commonly reported among trauma survivors. In the present study, we examined neural correlates of PTG in 106 veterans with PTSD and 193 veteran controls using task-free magnetoencephalography (MEG), diagnostic interviews and measures of PTG, and traumatic event exposure. Global synchronous neural interactions (SNIs) were significantly modulated downward with increasing PTG scores in controls (p = .005), but not in veterans with PTSD (p = .601). This effect was primarily characterized by negative slopes in local neural networks, was strongest in the medial prefrontal cortex, and was much stronger and more extensive in the control than the PTSD group. The present study complements previous research highlighting the role of neural adaptation in healthy functioning.
Extending results from their MEG studies of 2010, Brain Sciences Center researchers Peka Christova, Lisa M. James, Brian E. Engdahl, Scott M. Lewis and Apostolos P. Georgopoulos have published the paper "Diagnosis of posttraumatic stress disorder (PTSD) based on correlations of prewhitened fMRI data: outcomes and areas involved" in the journal Experimental Brain Research. Christova and her team successfully classified PTSD and control subjects using neural correlations from prewhitened resting-state fMRI data with 93.3% accuracy.
Successful diagnosis of PTSD has been achieved using neural correlations from prewhitened mag- netoencephalographic (MEG) time series (Georgopoulos et al. in J Neural Eng 7:16011, 2010. doi:10.1088/1741- 2560/7/1/016011; James et al. 2015). Here, we show that highly successful classification of PTSD and control sub- jects can be obtained using neural correlations from pre-whitened resting-state fMRI data. All but one PTSD (14/15; sensitivity = 93.3 %) and all but one control (20/21; speci- ficity = 95.2 %) subjects were correctly classified using 15 out of 2701 possible correlations between 74 brain areas. In contrast, correlations of the same but non-prewhitened data yielded chance-level classifications. We conclude that, if properly processed, fMRI has the prospect of aiding significantly in PTSD diagnosis. Twenty-five brain areas were most prominently involved in correct subject classification, including areas from all cortical lobes and the left pallidum.
BSC Director Apostolos P. Georgopoulos has published a paper in a volume to honor Vernon Mountcastle and Patricia Goldman-Rakic. The chapter, "Columnar Organization of the Motor Cortex: Direction of Movement" appears in the book Recent Organization of the Modular Advances on the Cortex.
The discovery by Vernon B. Mountcastle of the columnar organization of the cerebral cortex (Mountcastle VB, J Neurophysiol 20:408–434, 1957, Brain 120:701–722, 1997) was the single most important discovery of the twentieth century in cortical physiology. Not only did it serve as the framework for the orderly arrangement of knowledge concerning cortical organization and function (Edelman and Mountcastle, The mindful brain. MIT Press, Cambridge, MA, 1978) but also as a framework for exploring and investigating new ideas and for revisiting old ones about the organization of particular cortical areas. Here I review the history of facts and ideas about the organization of the motor cortex and discuss the evidence that the direction of movement is the principle governing motor cortical columnar organization.