Identification of individual subjects based on neuroanatomical measures obtained seven years earlier

We analyzed a dataset comprising 118 subjects who were scanned three times (at baseline, 1-year follow-up, and 7-year follow-up) using structural MRI over the course of seven years. We aimed to examine whether it is possible to identify individual subjects based on a restricted number of neuroanatomical features measured 7 years previously. We used FreeSurfer to compute 15 standard brain measures (total intracranial volume (ICV), total cortical thickness (CT), total cortical surface area (CA), cortical gray matter (CoGM), cerebral white matter (CeWM), cerebellar cortex (CBGM), cerebellar white matter (CBWM), subcortical volumes [thalamus, putamen, pallidum, caudatus, hippocampus, amygdala, accumbens], and brain stem volume). We used linear discriminant analysis (LDA), random forest machine learning (RF), and a newly developed rule-based identification approach (RBIA) for the identification process. Using RBIA, different sets of neuroanatomical features (ranging from 2 to 14) obtained at baseline were combined by if-then rules and compared to the same set of neuroanatomical features derived from the 7-year follow-up measurement. We achieved excellent identification results with LDA, while the identification results for RF were very good but not perfect. The RBIA produced the best results, achieving perfect participant identification for some 4-feature sets. The identification results improved substantially when using larger feature sets, with 14 neuroanatomical features providing perfect identification. Thus, this study shows again that the human brain is highly individual in terms of neuroanatomical features. These results are discussed in the context of the current literature on brain plasticity and the scientific attempts to develop brain-fingerprinting techniques.

Statistical learning and prosodic bootstrapping differentially affect neural synchronization during speech segmentation

Neural oscillations constitute an intrinsic property of functional brain organization that facilitates the tracking of linguistic units at multiple time scales through brain-to-stimulus alignment. This ubiquitous neural principle has been shown to facilitate speech segmentation and word learning based on statistical regularities. However, there is no common agreement yet on whether speech segmentation is mediated by a transition of neural synchronization from syllable to word rate, or whether the two time scales are concurrently tracked. Furthermore, it is currently unknown whether syllable transition probability contributes to speech segmentation when lexical stress cues can be directly used to extract word forms. Using Inter-Trial Coherence (ITC) analyses in combinations with Event-Related Potentials (ERPs), we showed that speech segmentation based on both statistical regularities and lexical stress cues was accompanied by concurrent neural synchronization to syllables and words. In particular, ITC at the word rate was generally higher in structured compared to random sequences, and this effect was particularly pronounced in the flat condition. Furthermore, ITC at the syllable rate dynamically increased across the blocks of the flat condition, whereas a similar modulation was not observed in the stressed condition. Notably, in the flat condition ITC at both time scales correlated with each other, and changes in neural synchronization were accompanied by a rapid reconfiguration of the P200 and N400 components with a close relationship between ITC and ERPs. These results highlight distinct computational principles governing neural synchronization to pertinent linguistic units while segmenting speech under different listening conditions.

Identification of individual subjects on the basis of their brain anatomical features

We examined whether it is possible to identify individual subjects on the basis of brain anatomical features. For this, we analyzed a dataset comprising 191 subjects who were scanned three times over a period of two years. Based on FreeSurfer routines, we generated three datasets covering 148 anatomical regions (cortical thickness, area, volume). These three datasets were also combined to a dataset containing all of these three measures. In addition, we used a dataset comprising 11 composite anatomical measures for which we used larger brain regions (11LBR). These datasets were subjected to a linear discriminant analysis (LDA) and a weighted K-nearest neighbors approach (WKNN) to identify single subjects. For this, we randomly chose a data subset (training set) with which we calculated the individual identification. The obtained results were applied to the remaining sample (test data). In general, we obtained excellent identification results (reasonably good results were obtained for 11LBR using WKNN). Using different data manipulation techniques (adding white Gaussian noise to the test data and changing sample sizes) still revealed very good identification results, particularly for the LDA technique. Interestingly, using the small 11LBR dataset also revealed very good results indicating that the human brain is highly individual.

Functional connectivity in the dorsal stream and between bilateral auditory-related cortical areas differentially contribute to speech decoding depending on spectro-temporal signal integrity and performance

Speech processing relies on the interdependence between auditory perception, sensorimotor integration, and verbal memory functions. Functional and structural connectivity between bilateral auditory-related cortical areas (ARCAs) facilitates spectro-temporal analyses, whereas the dynamic interplay between ARCAs and Broca's area (i.e., dorsal pathway) contributes to verbal memory functions, articulation, and sound-to-motor mapping. However, it remains unclear whether these two neural circuits are preferentially driven by spectral or temporal acoustic information, and whether their recruitment is predictive of speech perception performance and learning. Therefore, we evaluated EEG-based intracranial (eLORETA) functional connectivity (lagged coherence) in both pathways (i.e., between bilateral ARCAs and in the dorsal stream) while good- (GPs, N = 12) and poor performers (PPs, N = 13) learned to decode natural pseudowords (CLEAN) or comparable items (speech-noise chimeras) manipulated in the envelope (ENV) or in the fine-structure (FS). Learning to decode degraded speech was generally associated with increased functional connectivity in the theta, alpha, and beta frequency range in both circuits. Furthermore, GPs exhibited increased connectivity in the left dorsal stream compared to PPs, but only during the FS condition and in the theta frequency band. These results suggest that both pathways contribute to the decoding of spectro-temporal degraded speech by increasing the communication between brain regions involved in perceptual analyses and verbal memory functions. Otherwise, the left-hemispheric recruitment of the dorsal stream in GPs during the FS condition points to a contribution of this pathway to articulatory-based memory processes that are dependent on the temporal integrity of the speech signal. These results enable to better comprehend the neural circuits underlying word-learning as a function of temporal and spectral signal integrity and performance.

Increased functional connectivity in the ventral and dorsal streams during retrieval of novel words in professional musicians

Current models of speech and language processing postulate the involvement of two parallel processing streams (the dual stream model): a ventral stream involved in mapping sensory and phonological representations onto lexical and conceptual representations and a dorsal stream contributing to sound-to-motor mapping, articulation, and to how verbal information is encoded and manipulated in memory. Based on previous evidence showing that music training has an influence on language processing, cognitive functions, and word learning, we examined EEG-based intracranial functional connectivity in the ventral and dorsal streams while musicians and nonmusicians learned the meaning of novel words through picture-word associations. In accordance with the dual stream model, word learning was generally associated with increased beta functional connectivity in the ventral stream compared to the dorsal stream. In addition, in the linguistically most demanding "semantic task," musicians outperformed nonmusicians, and this behavioral advantage was accompanied by increased left-hemispheric theta connectivity in both streams. Moreover, theta coherence in the left dorsal pathway was positively correlated with the number of years of music training. These results provide evidence for a complex interplay within a network of brain regions involved in semantic processing and verbal memory functions, and suggest that intensive music training can modify its functional architecture leading to advantages in novel word learning.

Age prediction on the basis of brain anatomical measures

In this study, we examined whether age can be predicted on the basis of different anatomical features obtained from a large sample of healthy subjects (n = 3,144). From this sample we obtained different anatomical feature sets: (1) 11 larger brain regions (including cortical volume, thickness, area, subcortical volume, cerebellar volume, etc.), (2) 148 cortical compartmental thickness measures, (3) 148 cortical compartmental area measures, (4) 148 cortical compartmental volume measures, and (5) a combination of the above-mentioned measures. With these anatomical feature sets, we predicted age using 6 statistical techniques (multiple linear regression, ridge regression, neural network, k-nearest neighbourhood, support vector machine, and random forest). We obtained very good age prediction accuracies, with the highest accuracy being R²  = 0.84 (prediction on the basis of a neural network and support vector machine approaches for the entire data set) and the lowest being R²  = 0.40 (prediction on the basis of a k-nearest neighborhood for cortical surface measures). Interestingly, the easy-to-calculate multiple linear regression approach with the 11 large brain compartments resulted in a very good prediction accuracy (R²  = 0.73), whereas the application of the neural network approach for this data set revealed very good age prediction accuracy (R²  = 0.83). Taken together, these results demonstrate that age can be predicted well on the basis of anatomical measures. The neural network approach turned out to be the approach with the best results. In addition, it was evident that good prediction accuracies can be achieved using a small but nevertheless age-representative dataset of brain features. Hum Brain Mapp 38:997-1008, 2017. © 2016 Wiley Periodicals, Inc.