Cognitive control dysregulation is nearly universal across disorders, including major depressive disorder (MDD). Achieving comparable response rates to medication, the transcranial magnetic stimulation (TMS) mechanism and its effect on cognitive control have not been well understood yet. This paper investigates the predictive capability of the clinical response to TMS treatment using 34 cognitive variables measured from TMS treatment of 22 MDD subjects over an eight-week period. We employ a novel brain-inspired computing paradigm, hyperdimensional computing (HDC), to classify the effectiveness of TMS using leave-one-subject-out cross-validation (LOSOCV). Four performance metrics-accuracy, sensitivity, specificity and AUC-are used, with AUC being the primary metric. Experimental results reveal that: i). Although SVM outperforms HDC in terms of accuracy, HDC achieves an AUC of 0.82, surpassing SVM by 0.07. ii). The optimal performance for both classifiers is obtained with feature selection using SelectKBest. iii) Among the top features selected by SelectKBest for the two classifiers, ws_MedRT (median rate for the Websurf task) shows a more distinguishable distribution between clinical responses ("1") and no clinical responses ("0"). In conclusion, these results highlight the potential of HDC for predicting clinical responses to TMS and underscore the importance of feature selection in improving classification performance.

Cognitive processes, such as learning and cognitive flexibility, are both difficult to measure and to sample continuously using objective tools because cognitive processes arise from distributed, high-dimensional neural activity. For both research and clinical applications, that dimensionality must be reduced. To reduce dimensionality and measure underlying cognitive processes, we propose a modeling framework in which a cognitive process is defined as a low-dimensional dynamical latent variable—called a cognitive state, which links high-dimensional neural recordings and multidimensional behavioral readouts. This framework allows us to decompose the hard problem of modeling the relationship between neural and behavioral data into separable encoding-decoding approaches. We first use a state-space modeling framework, the behavioral decoder, to articulate the relationship between an objective behavioral readout (e.g., response times) and cognitive state. The second step, the neural encoder, involves using a generalized linear model (GLM) to identify the relationship between the cognitive state and neural signals, such as local field potential (LFP). We then use the neural encoder model and a Bayesian filter to estimate cognitive state using neural data (LFP power) to generate the neural decoder. We provide goodness-of-fit analysis and model selection criteria in support of the encoding-decoding result. We apply this framework to estimate an underlying cognitive state from neural data in human participants ([Formula: see text]) performing a cognitive conflict task. We successfully estimated the cognitive state within the 95% confidence intervals of that estimated using behavior readout for an average of 90% of task trials across participants. In contrast to previous encoder-decoder models, our proposed modeling framework incorporates LFP spectral power to encode and decode a cognitive state. The framework allowed us to capture the temporal evolution of the underlying cognitive processes, which could be key to the development of closed-loop experiments and treatments.

Direct electrical communication between living nervous systems and external devices would allow a wide variety of clinical and engineering applications. This neuro-robotic interface has not yet been fully achieved because existing interface systems cannot establish sufficiently close contact between neurons and communicating electrodes. We present here the synthesis and testing of a new class of conductive polymer that can be used to coat metal electrodes and achieve the necessary close contact. The polymer coatings can be made to incorporate biological adhesion proteins to maximize biocompatibility. Neurons extracted from mouse and rat brains were able to attach to the coatings, survive beyond five days, and grow out long, communicating processes. These new polymer films offer the potential for a new level of communication between robotic devices and the nervous system and may eventually make a high-bandwidth interface a reality.

High-throughput, data-directed computational protocols for Structural Genomics (or Proteomics) are required in order to evaluate the protein products of genes for structure and function at rates comparable to current gene-sequencing technology. This paper presents the JIGSAW algorithm, a novel high-throughput, automated approach to protein structure characterization with nuclear magnetic resonance (NMR). JIGSAW applies graph algorithms and probabilistic reasoning techniques, enforcing first-principles consistency rules in order to overcome a 5-10% signal-to-noise ratio. It consists of two main components: (1) graph-based secondary structure pattern identification in unassigned heteronuclear NMR data, and (2) assignment of spectral peaks by probabilistic alignment of identified secondary structure elements against the primary sequence. JIGSAW's deferment of assignment until after secondary structure identification differs greatly from traditional approaches, which begin by correlating peaks among dozens of experiments. By deferring assignment, JIGSAW not only eliminates this bottleneck, it also allows the number of experiments to be reduced from dozens to four, none of which requires 13 C-labeled protein. This in turn dramatically reduces the amount and expense of wet lab molecular biology for protein expression and purification, as well as the total spectrometer time to collect data.Our results for three test proteins demonstrate that we are able to identify and align approximately 80 percent of a-helical and 60 percent of b-sheet structure. JIGSAW is very fast, running in minutes on a Pentium-class Linux workstation. This approach yields quick and reasonably accurate (as opposed to the traditional slow and extremely accurate) structure calculations, utilizing a suite of graph analysis algorithms to compensate for the data sparseness. JIGSAW could be used for quick structural assays to speed data to the biologist early in the process of investigation, and could in principle be applied in an automation-like fashion to a large fraction of the proteome.