ABSTRACT Advances in high throughput sequencing (HTS) enables application of single nucleotide polymorphism (SNP) panels for identification and mixture analysis. Large reference sets of characterized individuals with documented relationships, ethnicities, and admixture are not yet available for characterizing the impacts of different ethnicities, kinships, and admixture on identification and mixture search results for relatives and unrelated individuals. Models for the expected results are presented with comparison results on two in silico datasets spanning four ethnicities, extended kinship relationships, and also admixture between the four ethnicities.

High throughput sequencing (HTS) of single nucleotide polymorphisms (SNPs) provides additional applications for DNA forensics including identification, mixture analysis, kinship prediction, and biogeographic ancestry prediction. Public repositories of human genetic data are being rapidly generated and released, but the majorities of these samples are de-identified to protect privacy, and have little or no individual metadata such as appearance (photos), ethnicity, relatives, etc. A reference in silico dataset has been generated to enable development and testing of new DNA forensics algorithms. This dataset provides 11 million SNP profiles for individuals with defined ethnicities and family relationships spanning eight generations with admixture for a panel with 39,108 SNPs.

The ability to track depression severity over time using passive sensing of speech would enable frequent and inexpensive monitoring, allowing rapid assessment of treatment efficacy as well as improved long term care of individuals at high risk for depression. In this paper an algorithm is proposed that estimates the articulatory coordination of speech from audio and video signals, and uses these coordination features to learn a prediction model to track depression severity with treatment. In addition, the algorithm is able to adapt its prediction model to an individual’s baseline data in order to improve tracking accuracy. The algorithm is evaluated on two data sets. The first is the Wyss Institute Biomarkers for Depression (WIBD) multi-modal data set, which includes audio and video speech recordings. The second data set was collected by Mundt et al (2007) and contains audio speech recordings only. The data sets are comprised of patients undergoing treatment for depression as well as control subjects. In its within-subject tracking of clinical Hamilton depression (HAM-D) ratings, the algorithm achieves root mean squared error (RMSE) of 5.49 with Spearman correlation of r = 0.63 on the WIBD data set, and achieves RMSE = 5.99 with r = 0.48 on the Mundt data set.

Analysis of DNA samples is an important tool in forensics, and the speed of analysis can impact investigations. Comparison of DNA sequences is based on the analysis of short tandem repeats (STRs), which are short DNA sequences of 2-5 base pairs. Current forensics approaches use 20 STR loci for analysis. The use of single nucleotide polymorphisms (SNPs) has utility for analysis of complex DNA mixtures. The use of tens of thousands of SNPs loci for analysis poses significant computational challenges because the forensic analysis scales by the product of the loci count and number of DNA samples to be analyzed. In this paper, we discuss the implementation of a DNA sequence comparison algorithm by re-casting the algorithm in terms of linear algebra primitives. By developing an overloaded matrix multiplication approach to DNA comparisons, we can leverage advances in GPU hardware and algoithms for dense matrix multiplication (DGEMM) to speed up DNA sample comparisons. We show that it is possible to compare 2048 unknown DNA samples with 20 million known samples in under 6 seconds using a NVIDIA K80 GPU.

ABSTRACT Accurate kinship predictions using DNA forensic samples has utility for investigative leads, remains identification, identifying relationships between individuals of interest, etc. High throughput sequencing (HTS) of STRs and single nucleotide polymorphisms (SNPs) is enabling the characterization of larger numbers of loci. Large panels of SNP loci have been proposed for improved mixture analysis of forensic samples. While multiple kinship prediction approaches have been established, we present an approach focusing on these large HTS SNP panels for predicting degree of kinship predictions. Formulas for first degree relatives can be multiplied (chained) together to model extended kinship relationships. Predictions are made using these formulations by calculating log likelihood ratios and selecting the maximum likelihood across the possible relationships. With a panel of 30,000 SNPs evaluated on an in silico dataset, this method can resolve parents from siblings and distinguish 1st, 2nd, and 3rd degree relatives from each other and unrelated individuals.

Abstract : A major goal of noninvasive brain sensing is to ascertain both the workload and the efficacy of cognitive processing. Realizing this goal will assist in monitoring cognitive readiness under different levels of cognitive workload and fatigue. Our approach to discriminating a persons cognitive state is predicated on the idea that cognition depends on coordinated neural activations, operating over a range of frequencies, that link functional networks across multiple brain regions. Therefore, our approach focuses on characterizing neural activation and connectivity patterns across the brain within multiple frequency bands. In each band, neural activations are characterized using spatial distributions of power across EEG channels, and neural connectivities are characterized using the eigenspectra of EEG connectivity matrices. The connectivity matrices are constructed using two measures: coherence and covariance. We use an auditory working memory task to vary cognitive workload by altering the number of digits held in memory during the simultaneous retention of a sentence in memory. Cognitive efficacy is assessed based on accuracy in recalling digits from memory. A Gaussian classifier is used to discriminate cognitive load and performance from EEG recorded during each experimental trial, and quantify discrimination accuracy with the area under the receiver operating characteristic curve (AUC) statistic. For cognitive load discrimination, AUC values of 0.59, 0.56, and 0.60 are obtained using power-, coherence-, and covariance-based feature sets, respectively. For cognitive performance discrimination, AUC values of 0.49, 0.62, and 0.63 are obtained for the same feature sets.

Studies in recent years have demonstrated that neural organization and structure impact an individual's ability to perform a given task. Specifically, individuals with greater neural efficiency have been shown to outperform those with less organized functional structure. In this work, we compare the predictive ability of properties of neural connectivity on a working memory task. We provide two novel approaches for characterizing functional network connectivity from electroencephalography (EEG), and compare these features to the average power across frequency bands in EEG channels. Our first novel approach represents functional connectivity structure through the distribution of eigenvalues making up channel coherence matrices in multiple frequency bands. Our second approach creates a connectivity network at each frequency band, and assesses variability in average path lengths of connected components and degree across the network. Failures in digit and sentence recall on single trials are detected using a Gaussian classifier for each feature set, at each frequency band. The classifier results are then fused across frequency bands, with the resulting detection performance summarized using the area under the receiver operating characteristic curve (AUC) statistic. Fused AUC results of 0.63/0.58/0.61 for digit recall failure and 0.58/0.59/0.54 for sentence recall failure are obtained from the connectivity structure, graph variability, and channel power features respectively.

Early, accurate detection of Parkinson’s disease may aid in possible intervention and rehabilitation. Thus, simple noninvasive biomarkers are desired for determining severity. In this study, a novel set of acoustic speech biomarkers are introduced and fused with conventional features for predicting clinical assessment of Parkinson’s disease. We introduce acoustic biomarkers reflecting the segment dependence of changes in speech production components, motivated by disturbances in underlying neural motor, articulatory, and prosodic brain centers of speech. Such changes occur at phonetic and larger time scales, including multi-scale perturbations in formant frequency and pitch trajectories, in phoneme durations and their frequency of occurrence, and in temporal waveform structure. We also introduce articulatory features based on a neural computational model of speech production, the Directions into Velocities of Articulators (DIVA) model. The database used is from the Interspeech 2015 Computational Paralinguistic Challenge. By fusing conventional and novel speech features, we obtain Spearman correlations between predicted scores and clinical assessments of r = 0.63 on the training set (four-fold cross validation), r = 0.70 on a held-out development set, and r = 0.97 on a held-out test set.

Early, accurate detection of cognitive load can help reduce risk of accidents and injuries, and inform intervention and rehabilitation in recovery. Thus, simple noninvasive biomarkers are desired for determining cognitive load under cognitively complex tasks. In this study, a novel set of vocal biomarkers are introduced for detecting different cognitive load conditions. Our vocal biomarkers use phoneme- and pseudosyllable-based measures, and articulatory and source coordination derived from cross-correlation and temporal coherence of formant and creakiness measures. A similar to 2-hour protocol was designed to induce cognitive load by stressing auditory working memory. This was done by repeatedly requiring the subject to recall a sentence while holding a number of digits in memory. We demonstrate the power of our speech features to discriminate between high and low load conditions. Using a database consisting of audio from 13 subjects, we apply classification models of cognitive load, showing a similar to 7% detection equal-error rate from features derived from 40 sentence utterances (similar to 4 minutes of audio).

For a forensic identification method to be admissible in international courts, the probability of false match must be quantified. For comparison of individuals against complex mixtures using a panel of single nucleotide polymorphisms (SNPs), the probability of a random man not excluded, P(RMNE) is one admissible standard. While the P(RMNE) of SNP alleles has been previously studied, it remains to be rigorously defined and calculated for experimentally genotyped mixtures. In this report, exact P(RMNE) values were calculated for a range of complex mixtures, verified with Monte Carlo simulations, and compared alongside experimentally determined detection probabilities.

Speech analysis has shown potential for identifying neurological impairment. With brain trauma, changes in brain structure or connectivity may result in changes in source, prosodic, or articulatory aspects of voice. In this work, we examine the articulatory components of speech reflected in formant tracks, and how changes in track dynamics and coordination map to cognitive decline. We address a population of athletes regularly receiving impacts to the head and showing signs of preclinical mild traumatic brain injury (mTBI), a state indicated by impaired cognitive performance occurring prior to concussion. We hypothesize that this preclinical damage results in 1) changes in average vocal tract dynamics measured by formant frequencies, their velocities, and acceleration, and 2) changes in articulatory coordination measured by a novel formant-frequency cross-correlation characterization. These features allow machine learning algorithms to detect preclinical mTBI identified by a battery of cognitive tests. A comparison is performed of the effectiveness of vocal tract dynamics features versus articulatory coordination features. This evaluation is done using receiver operating characteristic (ROC) curves along with confidence bounds. The articulatory dynamics features achieve area under the ROC curve (AUC) values between 0.72 and 0.98, whereas the articulatory coordination features achieve AUC values between 0.94 and 0.97.

In individuals with major depressive disorder, neurophysiological changes often alter motor control and thus affect the mechanisms controlling speech production and facial expression. These changes are typically associated with psychomotor retardation, a condition marked by slowed neuromotor output that is behaviorally manifested as altered coordination and timing across multiple motor-based properties. Changes in motor outputs can be inferred from vocal acoustics and facial movements as individuals speak. We derive novel multi-scale correlation structure and timing feature sets from audio-based vocal features and video-based facial action units from recordings provided by the 4th International Audio/Video Emotion Challenge (AVEC). The feature sets enable detection of changes in coordination, movement, and timing of vocal and facial gestures that are potentially symptomatic of depression. Combining complementary features in Gaussian mixture model and extreme learning machine classifiers, our multivariate regression scheme predicts Beck depression inventory ratings on the AVEC test set with a root-mean-square error of 8.12 and mean absolute error of 6.31. Future work calls for continued study into detection of neurological disorders based on altered coordination and timing across audio and video modalities.

In Major Depressive Disorder (MDD), neurophysiologic changes can alter motor control [1, 2] and therefore alter speech production by influencing the characteristics of the vocal source, tract, and prosodics. Clinically, many of these characteristics are associated with psychomotor retardation, where a patient shows sluggishness and motor disorder in vocal articulation, affecting coordination across multiple aspects of production [3, 4]. In this paper, we exploit such effects by selecting features that reflect changes in coordination of vocal tract motion associated with MDD. Specifically, we investigate changes in correlation that occur at different time scales across formant frequencies and also across channels of the delta-mel-cepstrum. Both feature domains provide measures of coordination in vocal tract articulation while reducing effects of a slowly-varying linear channel, which can be introduced by time-varying microphone placements. With these two complementary feature sets, using the AVEC 2013 depression dataset, we design a novel Gaussian mixture model (GMM)-based multivariate regression scheme, referred to as Gaussian Staircase Regression, that provides a root-mean-squared-error (RMSE) of 7.42 and a mean-absolute-error (MAE) of 5.75 on the standard Beck depression rating scale. We are currently exploring coordination measures of other aspects of speech production, derived from both audio and video signals.

In Major Depressive Disorder (MDD), neurophysiologic changes can alter motor control [1][2] and therefore alter speech production by influencing vocal fold motion (source), the vocal tract (system), and melody (prosody). In this paper, we use a database of voice recordings from 28 depressed subjects treated over a 6-week period [3] to compare correlations between features from each of the three speech-production components and clinical assessments of MDD. Toward biomarkers for audio-based continuous monitoring of depression severity, we explore the contextual dependence of these correlations with free-response and read speech, and show tradeoffs across categories of features in these two example contexts. Likewise, we also investigate the context-and speech component-dependence of correlations between our vocal features and assessment of individual symptoms of MDD (e.g., depressed mood, agitation, energy). Finally, motivated by our initial findings, we describe how context may be useful in “on-body” monitoring of MDD to facilitate identification of depression and evaluation of its treatment.

Neurophysiological changes in the brain associated with major depression disorder can disrupt articulatory precision in speech production. Motivated by this observation, we address the hypothesis that articulatory features, as manifested through formant frequency tracks, can help in automatically classifying depression state. Specifically, we investigate the relative importance of vocal tract formant frequencies and their dynamic features from sustained vowels and conversational speech. Using a database consisting of audio from 35 subjects with clinical measures of depression severity, we explore the performance of Gaussian mixture model (GMM) and support vector machine (SVM) classifiers. With only formant frequencies and their dynamics given by velocity and acceleration, we show that depression state can be classified with an optimal sensitivity/specificity/area under the ROC curve of 0.86/0.64/0.70 and 0.77/0.77/0.73 for GMMs and SVMs, respectively. Future work will involve merging our formant-based characterization with vocal source and prosodic features.

Recent research in brain-machine interfaces and devices to treat neurological disease indicate that important network activity exists at temporal and spatial scales beyond the resolution of existing implantable devices. High density, active electrode arrays hold great promise in enabling high-resolution interface with the brain to access and influence this network activity. Integrating flexible electronic devices directly at the neural interface can enable thousands of multiplexed electrodes to be connected using many fewer wires. Active electrode arrays have been demonstrated using flexible, inorganic silicon transistors. However, these approaches may be limited in their ability to be cost-effectively scaled to large array sizes (8×8 cm). Here we show amplifiers built using flexible organic transistors with sufficient performance for neural signal recording. We also demonstrate a pathway for a fully integrated, amplified and multiplexed electrode array built from these devices.