Imaging learned fear circuitry in awake mice using fMRI

Functional magnetic resonance imaging (fMRI) of learned behaviour in ‘awake rodents’ provides the opportunity for translational preclinical studies into the influence of pharmacological and genetic manipulations on brain function. fMRI has recently been employed to investigate learned behaviour in awake rats. Here, this methodology is translated to mice, so that future fMRI studies may exploit the vast number of genetically modified mouse lines that are available. One group of mice was conditioned to associate a flashing light (conditioned stimulus, CS) with foot shock (PG; paired group), and another group of mice received foot shock and flashing light explicitly unpaired (UG; unpaired group). The blood oxygen level-dependent signal (proxy for neuronal activation) in response to the CS was measured 24 h later in awake mice from the PG and UG using fMRI. The amygdala, implicated in fear processing, was activated to a greater degree in the PG than in the UG in response to the CS. Additionally, the nucleus accumbens was activated in the UG in response to the CS. Because the CS signalled an absence of foot shock in the UG, it is possible that this region is involved in processing the safety aspect of the CS. To conclude, the first use of fMRI to visualise brain activation in awake mice that are completing a learned emotional task is reported. This work paves the way for future preclinical fMRI studies to investigate genetic and environmental influences on brain function in transgenic mouse models of disease and aging

Machine learning of neuroimaging for assisted diagnosis of cognitive impairment and dementia: A systematic review

pp. 519-535Los métodos avanzados de aprendizaje automático pueden ayudar a identificar el riesgo de demencia de la neuroimagen, pero su precisión hasta la fecha no está clara. Revisamos sistemáticamente la literatura, desde 2006 hasta finales de 2016, para los estudios de aprendizaje automático que diferencian el envejecimiento saludable de la demencia de varios tipos, evaluamos la calidad del estudio y comparamos la precisión en diferentes límites de enfermedades. De los 111 estudios relevantes, la mayoría evaluó la enfermedad de Alzheimer en comparación con los controles sanos, utilizando datos de la Iniciativa de neuroimagen AD, máquinas de vectores de soporte y solo secuencias ponderadas en T1. La precisión fue más alta para diferenciar la enfermedad de Alzheimer de los controles sanos y pobre para diferenciar los controles sanos versus deterioro cognitivo leve versus enfermedad de Alzheimer o conversores de deterioro cognitivo leve versus no conversores. La precisión aumentó con los tipos de datos combinados, pero no con la fuente de datos, el tamaño de la muestra o el método de aprendizaje automático. El aprendizaje automático todavía no distingue categorías de enfermedades clínicamente relevantes. Los conjuntos de datos más diversos, las combinaciones de diferentes tipos de datos y la estrecha integración clínica del aprendizaje automático ayudarían a avanzar en este campo.S

Parametric study of EEG sensitivity to phase noise during face processing

<b>Background: </b> The present paper examines the visual processing speed of complex objects, here faces, by mapping the relationship between object physical properties and single-trial brain responses. Measuring visual processing speed is challenging because uncontrolled physical differences that co-vary with object categories might affect brain measurements, thus biasing our speed estimates. Recently, we demonstrated that early event-related potential (ERP) differences between faces and objects are preserved even when images differ only in phase information, and amplitude spectra are equated across image categories. Here, we use a parametric design to study how early ERP to faces are shaped by phase information. Subjects performed a two-alternative force choice discrimination between two faces (Experiment 1) or textures (two control experiments). All stimuli had the same amplitude spectrum and were presented at 11 phase noise levels, varying from 0% to 100% in 10% increments, using a linear phase interpolation technique. Single-trial ERP data from each subject were analysed using a multiple linear regression model. <b>Results: </b> Our results show that sensitivity to phase noise in faces emerges progressively in a short time window between the P1 and the N170 ERP visual components. The sensitivity to phase noise starts at about 120–130 ms after stimulus onset and continues for another 25–40 ms. This result was robust both within and across subjects. A control experiment using pink noise textures, which had the same second-order statistics as the faces used in Experiment 1, demonstrated that the sensitivity to phase noise observed for faces cannot be explained by the presence of global image structure alone. A second control experiment used wavelet textures that were matched to the face stimuli in terms of second- and higher-order image statistics. Results from this experiment suggest that higher-order statistics of faces are necessary but not sufficient to obtain the sensitivity to phase noise function observed in response to faces. <b>Conclusion: </b> Our results constitute the first quantitative assessment of the time course of phase information processing by the human visual brain. We interpret our results in a framework that focuses on image statistics and single-trial analyses

The Glasgow Voice Memory Test: Assessing the ability to memorize and recognize unfamiliar voices

One thousand one hundred and twenty subjects as well as a developmental phonagnosic subject (KH) along with age-matched controls performed the Glasgow Voice Memory Test, which assesses the ability to encode and immediately recognize, through an old/new judgment, both unfamiliar voices (delivered as vowels, making language requirements minimal) and bell sounds. The inclusion of non-vocal stimuli allows the detection of significant dissociations between the two categories (vocal vs. non-vocal stimuli). The distributions of accuracy and sensitivity scores (d’) reflected a wide range of individual differences in voice recognition performance in the population. As expected, KH showed a dissociation between the recognition of voices and bell sounds, her performance being significantly poorer than matched controls for voices but not for bells. By providing normative data of a large sample and by testing a developmental phonagnosic subject, we demonstrated that the Glasgow Voice Memory Test, available online and accessible fromall over the world, can be a valid screening tool (~5 min) for a preliminary detection of potential cases of phonagnosia and of “super recognizers” for voices

Testing for the Dual-Route Cascade Reading Model in the Brain: An fMRI Effective Connectivity Account of an Efficient Reading Style

Neuropsychological data about the forms of acquired reading impairment provide a strong basis for the theoretical framework of the dual-route cascade (DRC) model which is predictive of reading performance. However, lesions are often extensive and heterogeneous, thus making it difficult to establish precise functional anatomical correlates. Here, we provide a connective neural account in the aim of accommodating the main principles of the DRC framework and to make predictions on reading skill. We located prominent reading areas using fMRI and applied structural equation modeling to pinpoint distinct neural pathways. Functionality of regions together with neural network dissociations between words and pseudowords corroborate the existing neuroanatomical view on the DRC and provide a novel outlook on the sub-regions involved. In a similar vein, congruent (or incongruent) reliance of pathways, that is reliance on the word (or pseudoword) pathway during word reading and on the pseudoword (or word) pathway during pseudoword reading predicted good (or poor) reading performance as assessed by out-of-magnet reading tests. Finally, inter-individual analysis unraveled an efficient reading style mirroring pathway reliance as a function of the fingerprint of the stimulus to be read, suggesting an optimal pattern of cerebral information trafficking which leads to high reading performance

Electrophysiological evidence for an early processing of human voices

<p>Abstract</p> <p>Background</p> <p>Previous electrophysiological studies have identified a "voice specific response" (VSR) peaking around 320 ms after stimulus onset, a latency markedly longer than the 70 ms needed to discriminate living from non-living sound sources and the 150 ms to 200 ms needed for the processing of voice paralinguistic qualities. In the present study, we investigated whether an early electrophysiological difference between voice and non-voice stimuli could be observed.</p> <p>Results</p> <p>ERPs were recorded from 32 healthy volunteers who listened to 200 ms long stimuli from three sound categories - voices, bird songs and environmental sounds - whilst performing a pure-tone detection task. ERP analyses revealed voice/non-voice amplitude differences emerging as early as 164 ms post stimulus onset and peaking around 200 ms on fronto-temporal (positivity) and occipital (negativity) electrodes.</p> <p>Conclusion</p> <p>Our electrophysiological results suggest a rapid brain discrimination of sounds of voice, termed the "fronto-temporal positivity to voices" (FTPV), at latencies comparable to the well-known face-preferential N170.</p

Age-related delay in information accrual for faces: Evidence from a parametric, single-trial EEG approach

Background: In this study, we quantified age-related changes in the time-course of face processing by means of an innovative single-trial ERP approach. Unlike analyses used in previous studies, our approach does not rely on peak measurements and can provide a more sensitive measure of processing delays. Young and old adults (mean ages 22 and 70 years) performed a non-speeded discrimination task between two faces. The phase spectrum of these faces was manipulated parametrically to create pictures that ranged between pure noise (0% phase information) and the undistorted signal (100% phase information), with five intermediate steps. Results: Behavioural 75% correct thresholds were on average lower, and maximum accuracy was higher, in younger than older observers. ERPs from each subject were entered into a single-trial general linear regression model to identify variations in neural activity statistically associated with changes in image structure. The earliest age-related ERP differences occurred in the time window of the N170. Older observers had a significantly stronger N170 in response to noise, but this age difference decreased with increasing phase information. Overall, manipulating image phase information had a greater effect on ERPs from younger observers, which was quantified using a hierarchical modelling approach. Importantly, visual activity was modulated by the same stimulus parameters in younger and older subjects. The fit of the model, indexed by R2, was computed at multiple post-stimulus time points. The time-course of the R2 function showed a significantly slower processing in older observers starting around 120 ms after stimulus onset. This age-related delay increased over time to reach a maximum around 190 ms, at which latency younger observers had around 50 ms time lead over older observers. Conclusion: Using a component-free ERP analysis that provides a precise timing of the visual system sensitivity to image structure, the current study demonstrates that older observers accumulate face information more slowly than younger subjects. Additionally, the N170 appears to be less face-sensitive in older observers

Longitudinal multi-centre brain imaging studies: guidelines and practical tips for accurate and reproducible imaging endpoints and data sharing

Abstract Background Research involving brain imaging is important for understanding common brain diseases. Study endpoints can include features and measures derived from imaging modalities, providing a benchmark against which other phenotypical data can be assessed. In trials, imaging data provide objective evidence of beneficial and adverse outcomes. Multi-centre studies increase generalisability and statistical power. However, there is a lack of practical guidelines for the set-up and conduct of large neuroimaging studies. Methods We address this deficit by describing aspects of study design and other essential practical considerations that will help researchers avoid common pitfalls and data loss. Results The recommendations are grouped into seven categories: (1) planning, (2) defining the imaging endpoints, developing an imaging manual and managing the workflow, (3) performing a dummy run and testing the analysis methods, (4) acquiring the scans, (5) anonymising and transferring the data, (6) monitoring quality, and (7) using structured data and sharing data. Conclusions Implementing these steps will lead to valuable and usable data and help to avoid imaging data wastage

The Open-Access European Prevention of Alzheimer's Dementia (EPAD) MRI dataset and processing workflow

The European Prevention of Alzheimer Dementia (EPAD) is a multi-center study that aims to characterize the preclinical and prodromal stages of Alzheimer's Disease. The EPAD imaging dataset includes core (3D T1w, 3D FLAIR) and advanced (ASL, diffusion MRI, and resting-state fMRI) MRI sequences. Here, we give an overview of the semi-automatic multimodal and multisite pipeline that we developed to curate, preprocess, quality control (QC), and compute image-derived phenotypes (IDPs) from the EPAD MRI dataset. This pipeline harmonizes DICOM data structure across sites and performs standardized MRI preprocessing steps. A semi-automated MRI QC procedure was implemented to visualize and flag MRI images next to site-specific distributions of QC features - i.e. metrics that represent image quality. The value of each of these QC features was evaluated through comparison with visual assessment and step-wise parameter selection based on logistic regression. IDPs were computed from 5 different MRI modalities and their sanity and potential clinical relevance were ascertained by assessing their relationship with biological markers of aging and dementia. The EPAD v1500.0 data release encompassed core structural scans from 1356 participants 842 fMRI, 831 dMRI, and 858 ASL scans. From 1356 3D T1w images, we identified 17 images with poor quality and 61 with moderate quality. Five QC features - Signal to Noise Ratio (SNR), Contrast to Noise Ratio (CNR), Coefficient of Joint Variation (CJV), Foreground-Background energy Ratio (FBER), and Image Quality Rate (IQR) - were selected as the most informative on image quality by comparison with visual assessment. The multimodal IDPs showed greater impairment in associations with age and dementia biomarkers, demonstrating the potential of the dataset for future clinical analyses