Optimal use of EEG recordings to target active brain areas with transcranial electrical stimulation

To demonstrate causal relationships between brain and behavior, investigators would like to guide brain stimulationusing measurements of neural activity. Particularly promising in this context are electroencephalography (EEG) and transcranial electrical stimulation (TES), as they are linked by a reciprocity principle which, despite being known for decades, has not led to a formalism for relating EEG recordings to optimal stimulation parameters. Here we derive a closed-form expression for the TES configuration that optimally stimulates (i.e., targets) the sources of recorded EEG, without making assumptions about source location or distribution. We also derive a duality between TES targeting and EEG source localization, and demonstrate that in cases where source localization fails, so does the proposed targeting. Numerical simulations with multiple head models confirm these theoretical predictions and quantify the achieved stimulation in terms of focality and intensity. We show that constraining the stimulation currents automatically selects optimal montages that involve only a few (4−7) electrodes, with only incremental loss in performance when targeting focal activations. The proposed technique allows brain scientists and clinicians to rationally target the sources of observed EEG and thus overcomes a major obstacle to the realization of individualized or closed-loop brain stimulation

Correlated Components Analysis - Extracting Reliable Dimensions in Multivariate Data

How does one find dimensions in multivariate data that are reliably expressed across repetitions? For example, in a brain imaging study one may want to identify combinations of neural signals that are reliably expressed across multiple trials or subjects. For a behavioral assessment with multiple ratings, one may want to identify an aggregate score that is reliably reproduced across raters. Correlated Components Analysis (CorrCA) addresses this problem by identifying components that are maximally correlated between repetitions (e.g. trials, subjects, raters). Here we formalize this as the maximization of the ratio of between-repetition to within-repetition covariance. We show that this criterion maximizes repeat-reliability, defined as mean over variance across repeats, and that it leads to CorrCA or to multi-set Canonical Correlation Analysis, depending on the constraints. Surprisingly, we also find that CorrCA is equivalent to Linear Discriminant Analysis for zero-mean signals, which provides an unexpected link between classic concepts of multivariate analysis. We present an exact parametric test of statistical significance based on the F-statistic for normally distributed independent samples, and present and validate shuffle statistics for the case of dependent samples. Regularization and extension to non-linear mappings using kernels are also presented. The algorithms are demonstrated on a series of data analysis applications, and we provide all code and data required to reproduce the results

Elevated cystatin-C concentration is associated with progression to prediabetes: the Western New York Study

OBJECTIVE – We conducted a nested case-control investigation to examine if elevated baseline concentrations of cystatin-C predicted progression from normoglycaemia to prediabetes over 6 years of follow-up from the Western New York Health Study. RESEARCH DESIGN AND METHODS – 1,455 participants from the Western New York Health Study, free of type 2 diabetes and known cardiovascular disease at baseline (1996-2001), were reexamined in 2002-2004. An incident case of prediabetes was defined as one with fasting glucose below 100 mg/dl at the baseline examination and ≥ 100 mg/dl and ≤ 125 mg/dl at the follow-up examination. All cases (n=91) were matched 1:3 to control participants based upon sex, race/ethnicity and year of study enrollment. All controls had fasting glucose levels < 100 mg/dl at both baseline and follow-up examinations. Cystatin-C concentrations and the urinary albumin to creatinine ratio were measured from frozen (-196 Cº) baseline blood and urine samples. Serum creatinine concentrations were available from the baseline examination. RESULTS –Multivariate conditional logistic regression analyses adjusted for age, baseline glucose level, HOMA-IR, body mass index, hypertension, eGFR, cigarette smoking, and alcohol use revealed a significantly increased risk of progression to prediabetes among those with elevated baseline concentrations of cystatin-C (Odds Ratio, 95% CI: 3.04, 1.34, 6.89) (upper quintile vs. the remainder). Results of secondary analyses that considered hs-CRP, IL-6, E-selectin, or sICAM did not alter these results. CONCLUSIONS - These results suggest that early renal impairment indexed with cystatin-C imparted a three-fold excess risk of progression to prediabetes in this study population. Recent evidence from randomized clinical trials (1,2) among people with prediabetes have provided convincing evidence that early intervention can significantly delay or prevent the progression to type 2 diabetes. The identification of those with prediabetes is assuming greater importance (3) especially in light of the fact that approximately 35 million adults aged 40-74 years old in the United States have prediabetes defined as impaired fasting glucose (4). Microalbuminuria occurs frequently in nondiabetic subjects and places them at increased risk for cardiovascular disease (5-7). The mechanisms behind this observation are poorly understood, however. Albuminuria may reflect underlying vascular damage (8), hypertension (9, 10) endothelial dysfunction (11, 12) and/or low-grade inflammation (13). A large percentage of type 2 individuals pass through a period of prediabetes (14) and may experience early renal dysfunction e.g., a glomerular filtration rate (GFR) above 60 ml/minute per 1.73m2. Currently used estimating equations are poor at identifying early renal impairment and better indices are of great interest (15, 16). Recently, several studies have suggested that cystatin-C levels may be a more sensitive marker of early renal impairment than either albuminuria or serum creatinine concentration (17-20). Therefore, a better understanding of a putative role for cystatin-C in the etiology of prediabetes could shed light on the renal/heart disease connection (21). Given the reported superiority of cystatin C over conventional measures of renal function, we hypothesized that cystatin-C would predict progression to prediabetes independent of serum creatinine or estimated GFR. We also investigated the role of intervening mechanisms including hypertension, insulin resistance, endothelial dysfunction and inflammation