Summary We present a high-dimensional model of the representational space in human ventral temporal (VT) cortex in which dimensions are response-tuning functions that are common across individuals and patterns of response are modeled as weighted sums of basis patterns associated with these response-tunings. We map response pattern vectors, measured with fMRI, from individual subjects’ voxel spaces into this common model space using a new method, ‘hyperalignment’. Hyperalignment parameters based on responses during one experiment – movie-viewing – identified 35 common response-tuning functions that captured fine-grained distinctions among a wide range of stimuli in the movie and in two category perception experiments. Between-subject classification (BSC, multivariate pattern classification based on other subjects’ data) of response pattern vectors in common model space greatly exceeded BSC of anatomically-aligned responses and matched within-subject classification. Results indicate that population codes for complex visual stimuli in VT cortex are based on response-tuning functions that are common across individuals.

The goal of the 2016 PhysioNet/CinC Challenge is the development of an algorithm to classify normal/abnormal heart sounds. A total of 124 time-frequency features were extracted from the phonocardiogram (PCG) and input to a variant of the AdaBoost classifier. A second classifier using convolutional neural network (CNN) was trained using PCGs cardiac cycles decomposed into four frequency bands. The final decision rule to classify normal/abnormal heart sounds was based on an ensemble of classifiers combining the outputs of AdaBoost and the CNN. The algorithm was trained on a training dataset (normal= 2575, abnormal= 665) and evaluated on a blind test dataset. Our classifier ensemble approach obtained the highest score of the competition with a sensitivity, specificity, and overall score of 0.9424, 0.7781, and 0.8602, respectively. IntroductionHeart auscultation is the primary tool for screening and diagnosis in primary health care [1]. Availability of digital stethoscopes and mobile devices provides clinicians an opportunity to record and analyze heart sounds (PCG) for diagnostic purposes. The goal of the 2016 PhysioNet/CinC Challenge is the development of algorithms to classify normal/abnormal heart sound recordings [2]. We proposed an ensemble of a feature-based classifier and a deep learningbased classifier to boost the classification performance of heart sounds. Method and MaterialA block diagram of the proposed approach to classify normal/abnormal PCG is shown in Fig. 1. Challenge DatabaseThe challenge database provided PCG recordings of healthy subjects and pathological patients collected at either a clinical or non-clinical environment. Details about the challenge dataset can be found in [2]. For algorithm development, in-house training and test sets were generated by randomly taking 80% and 20% of the records from each database, while keeping the same prevalence of abnormal classes. In-house training set was used for training and cross-validation of different models, and in-house test set was used for evaluation of the classification performance independently from the blind test dataset. Pre-processingEach PCG was resampled to 1000 Hz, band-pass filtered between 25 Hz and 400 Hz, and then pre-processed to remove any spikes in the PCG [3]. Furthermore, preprocessed PCGs were segmented into four heart sound states using a segmentation method proposed by Springer et al. [4]. Each PCG is comprised of more than one cardiac cycle (beat), and each beat is comprised of four heart sound states (i.e. S1, systole, S2, and diastole). Feature-based ApproachIn this approach, a variant of AdaBoost classifier [5] was trained for classification of normal/abnormal PCGs using time and frequency-domain features. Time-domain FeaturesMean and standard deviation (SD) of the following parameters were used as time-domain features (36 features): 1. PCG intervals: RR intervals, S1 intervals, S2 intervals, systolic intervals, diastolic intervals, ratio of systolic interval to RR interval of each heart beat, ratio of diastolic...

Making conclusions about the functional neuroanatomical organization of the human brain requires methods for relating the functional anatomy of an individual's brain to population variability. We have developed a method for aligning the functional neuroanatomy of individual brains based on the patterns of neural activity that are elicited by viewing a movie. Instead of basing alignment on functionally defined areas, whose location is defined as the center of mass or the local maximum response, the alignment is based on patterns of response as they are distributed spatially both within and across cortical areas. The method is implemented in the two-dimensional manifold of an inflated, spherical cortical surface. The method, although developed using movie data, generalizes successfully to data obtained with another cognitive activation paradigm—viewing static images of objects and faces—and improves group statistics in that experiment as measured by a standard general linear model (GLM) analysis.

Inter-subject alignment of functional MRI (fMRI) data is necessary for group analyses. The standard approach to this problem matches anatomical features of the brain, such as major anatomical landmarks or cortical curvature. Precise alignment of functional cortical topographies, however, cannot be derived using only anatomical features. We propose a new inter-subject registration algorithm that aligns intra-subject patterns of functional connectivity across subjects. We derive functional connectivity patterns by correlating fMRI BOLD time-series, measured during movie viewing, between spatially remote cortical regions. We validate our technique extensively on real fMRI experimental data and compare our method to two state-of-the-art inter-subject registration algorithms. By cross-validating our method on independent datasets, we show that the derived alignment generalizes well to other experimental paradigms.