Nice dipy paper: Aligning streamlines to streamline space outperforms image-based registration→

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In this paper, we introduce a novel, robust and efficient framework to align bundles of streamlines directly in the space of streamlines. We call this framework Streamline-based Linear Registration. We first show that this method can be used successfully to align individual bundles as well as whole brain streamlines. Additionally, if used as a piecewise linear registration across many bundles, we show that our novel method systematically provides higher overlap (Jaccard indices) than state-of-the-art nonlinear image-based registration in the white matter.

Test-retest reliability of dynamic causal modeling for fMRI→

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Using classical DCM (cDCM) in SPM5, we found that the test-retest reliability of DCM results was high, both concerning the model evidence (ICC = 0.94) and the model parameter estimates (median ICC = 0.47). However, when using a more recent DCM version (DCM10 in SPM8), test-retest reliability was reduced notably.

Optimization of rs-fMRI preprocessing for signal-Noise separation, test-retest reliability, group discrimination→

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We focus our analyses on the effects of common preprocessing steps, such as global signal regression (GS) (Weissenbacher et al., 2009 and Shirer et al., 2012); removal of cerebrospinal fluid (CSF) and white matter (WM) confounds (Shirer et al., 2012); noise regression of motion parameters estimated during motion correction (Friston et al., 1996, Power et al., 2012, Power et al., 2013, Satterthwaite et al., 2013 and Yan et al., 2013a); and temporal filtering at various frequency bands reported in the literature (Achard et al., 2006, Ko et al., 2011 and Guo et al., 2012).

Diffusion tensor imaging of dolphin brains reveals direct auditory pathway to temporal lobe→

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Very cool: (postmortem) dolphin DTI.

Using thalamic parcellation based on traditionally defined regions for the primary visual (V1) and auditory cortex (A1), we found distinct regions of the thalamus connected to V1 and A1. But in addition to suprasylvian-A1, we report here, for the first time, the auditory cortex also exists in the temporal lobe, in a region near cetacean-A2 and possibly analogous to the primary auditory cortex in related terrestrial mammals (Artiodactyla). Using probabilistic tract tracing, we found a direct pathway from the inferior colliculus to the medial geniculate nucleus to the temporal lobe near the sylvian fissure.

Transient hearing loss in a critical period leads to altered auditory cortex→

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To examine whether the cellular properties could recover from HL, earplugs were removed prior to (P17) or after (P23), the closure of these CPs. The earlier age of hearing restoration led to greater recovery of cellular function, but firing rate remained disrupted. When earplugs were removed after the closure of these CPs, several changes persisted into adulthood.

Anterior cingulate signals errors of attentional control before prefrontal-cingulate inhibition→

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Here, we tested this prediction by recording cells in the dACC and lateral prefrontal cortex (latPFC) of macaques performing an attention task that dissociated 3 processing stages. We found that, across prefrontal subareas, the dACC contained the largest cell populations encoding errors indicating (1) failures of inhibitory control of the attentional focus, (2) failures to prevent bottom-up distraction, and (3) lapses when implementing a choice. Error-locked firing in the dACC showed the earliest latencies across the PFC, emerged earlier than reward omission signals, and involved a significant proportion of putative inhibitory interneurons. Moreover, early onset error-locked response enhancement in the dACC was followed by transient prefrontal-cingulate inhibition, possibly reflecting active disengagement from task processing.

Simultaneous brain/cervical cord fMRI reveals spinal cord involvement in motor learning→

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Impressive technical achievement.

Here, for the first time, we provide evidence for local learning-induced plasticity in intact human spinal cord through simultaneous functional magnetic resonance imaging of the brain and spinal cord during motor sequence learning. Specifically, we show learning-related modulation of activity in the C6–C8 spinal region, which is independent from that of related supraspinal sensorimotor structures. Moreover, a brain–spinal cord functional connectivity analysis demonstrates that the initial linear relationship between the spinal cord and sensorimotor cortex gradually fades away over the course of motor sequence learning, while the connectivity between spinal activity and cerebellum gains strength. T

Prediction across sensory modalities: A neurocomputational model of the McGurk effect ($)→

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Here we assessed the role of dynamic cross-modal predictions in the outcome of AV speech integration using a computational model that processes continuous audiovisual speech sensory inputs in a predictive coding framework. The model involves three processing levels: sensory units, units that encode the dynamics of stimuli, and multimodal recognition/identity units. The model exhibits a dynamic prediction behavior because evidence about speech tokens can be asynchronous across sensory modality, allowing for updating the activity of the recognition units from one modality while sending top–down predictions to the other modality. We explored the model's response to congruent and incongruent AV stimuli and found that, in the two-dimensional feature space spanned by the speech second formant and lip aperture, fusion stimuli are located in the neighborhood of congruent /ada/, which therefore provides a valid match. Conversely, stimuli that lead to combination percepts do not have a unique valid neighbor. In that case, acoustic and visual cues are both highly salient and generate conflicting predictions in the other modality that cannot be fused, forcing the elaboration of a combinatorial solution.

Review: Neural reorganization and compensation in aging→

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Associations between additional recruitment and better performance in older adults have led to the suggestion that the additional recruitment may contribute to preserved cognitive function in old age and may explain some of the variation among individuals in preservation of function. However, many alternative explanations are possible, and recent findings and methodological developments have highlighted the need for more systematic approaches to determine whether reorganization occurs with age and whether it benefits performance. We reevaluate current evidence for compensatory functional reorganization in the light of recent moves to address these challenges.

Flexible information coding in auditory cortex during perception, imagery, and memory for sounds→

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Auditory imagery of the same sounds evokes similar overall activity in auditory cortex as perception. However, during imagery abstracted, categorical information is encoded in the neural patterns, particularly when individuals are experiencing more vivid imagery. This highlights the necessity to move beyond traditional “brain mapping” inference in human neuroimaging, which assumes common regional activation implies similar mental representations.

Review of deep learning ($)→

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All the rage these days; looks like a good overview. From the abstract:

Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.

Rhythmic auditory cortex activity shapes stimulus–response gain and background firing→

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We found that phase-dependent models better reproduced the observed responses than static models, during both stimulation with a series of natural sounds and epochs of silence. This was attributable to two factors: (1) phase-dependent variations in background firing (most prominent for delta; 1–4 Hz); and (2) modulations of response gain that rhythmically amplify and attenuate the responses at specific phases of the rhythm (prominent for frequencies between 2 and 12 Hz).