Fitzsimmonsmiller2605

Additionally, we identified a sensorimotor circuit, in which the functional connectivity among these regions was significantly correlated with levels of consciousness across the different groups, and remained present in the REM-sleep group. Taken together, we demonstrated that regions forming a higher-order sensorimotor integration circuit are involved in supporting consciousness within the brain's global functional network. That offers novel and more mechanism-guided treatment targets for disorders of consciousness.Recent advances in automated face recognition algorithms have increased the risk that de-identified research MRI scans may be re-identifiable by matching them to identified photographs using face recognition. A variety of software exist to de-face (remove faces from) MRI, but their ability to prevent face recognition has never been measured and their image modifications can alter automated brain measurements. In this study, we compared three popular de-facing techniques and introduce our mri_reface technique designed to minimize effects on brain measurements by replacing the face with a population average, rather than removing it. For each technique, we measured 1) how well it prevented automated face recognition (i.e. effects on exceptionally-motivated individuals) and 2) how it altered brain measurements from SPM12, FreeSurfer, and FSL (i.e. effects on the average user of de-identified data). Before de-facing, 97% of scans from a sample of 157 volunteers were correctly matched to photographs using automated face recognition. After de-facing with popular software, 28-38% of scans still retained enough data for successful automated face matching. Our proposed mri_reface had similar performance with the best existing method (fsl_deface) at preventing face recognition (28-30%) and it had the smallest effects on brain measurements in more pipelines than any other, but these differences were modest.Sensorimotor alpha suppression is present both during the observation and execution of actions, and is a commonly used tool to investigate neural mirroring in infancy. Köster et al. (2020) used this measure to investigate infants' motor cortex activation during the observation of action demonstrations and its relationship to subsequent imitation of these actions. Contrary to what is implied in the paper and to common findings in the literature, the study's results appear to suggest that the motor system was deactivated during the observation of the actions, and that greater deactivation during action observation was associated with a greater tendency to copy the action. Here we present potential methodological explanations for these unexpected findings and discuss them in relation to common recommendations in the field.The ability to access brain information in real-time is crucial both for a better understanding of cognitive functions and for the development of therapeutic applications based on brain-machine interfaces. Great success has been achieved in the field of neural motor prosthesis. Progress is still needed in the real-time decoding of higher-order cognitive processes such as covert attention. Recently, we showed that we can track the location of the attentional spotlight using classification methods applied to prefrontal multi-unit activity (MUA) in the non-human primates. Importantly, we demonstrated that the decoded (x,y) attentional spotlight parametrically correlates with the behavior of the monkeys thus validating our decoding of attention. We also demonstrate that this spotlight is extremely dynamic. Here, in order to get closer to non-invasive decoding applications, we extend our previous work to local field potential signals (LFP). Specifically, we achieve, for the first time, high decoding accuracy of the (x,y) location of the attentional spotlight from prefrontal LFP signals, to a degree comparable to that achieved from MUA signals, and we show that this LFP content is predictive of behavior. This LFP attention-related information is maximal in the gamma band (30-250 Hz), peaking between 60 to 120 Hz. In addition, we introduce a novel two-step decoding procedure based on the labelling of maximally attention-informative trials during the decoding procedure. This procedure strongly improves the correlation between our real-time MUA and LFP based decoding and behavioral performance, thus further refining the functional relevance of this real-time decoding of the (x,y) locus of attention. This improvement is more marked for LFP signals than for MUA signals. Overall, this study demonstrates that the attentional spotlight can be accessed from LFP frequency content, in real-time, and can be used to drive high-information content cognitive brain-machine interfaces for the development of new therapeutic strategies.All writing systems represent units of spoken language. Studies on the neural correlates of reading in different languages show that this skill relies on access to brain areas dedicated to speech processing. Speech-reading convergence onto a common perisylvian network is therefore considered universal among different writing systems. Using fMRI, we test whether this holds true also for tactile Braille reading in the blind. The neural networks for Braille and visual reading overlapped in the left ventral occipitotemporal (vOT) cortex. Even though we showed similar perisylvian specialization for speech in both groups, blind subjects did not engage this speech system for reading. In contrast to the sighted, speech-reading convergence in the blind was absent in the perisylvian network. Instead, the blind engaged vOT not only in reading but also in speech processing. The involvement of the vOT in speech processing and its engagement in reading in the blind suggests that vOT is included in a modality independent language network in the blind, also evidenced by functional connectivity results. The analysis of individual speech-reading convergence suggests that there may be segregated neuronal populations in the vOT for speech processing and reading in the blind.A key goal in neuroscience is to understand brain mechanisms of cognitive functions. selleck An emerging approach is "brain decoding", which consists of inferring a set of experimental conditions performed by a participant, using pattern classification of brain activity. Few works so far have attempted to train a brain decoding model that would generalize across many different cognitive tasks drawn from multiple cognitive domains. To tackle this problem, we proposed a multidomain brain decoder that automatically learns the spatiotemporal dynamics of brain response within a short time window using a deep learning approach. We evaluated the decoding model on a large population of 1200 participants, under 21 different experimental conditions spanning six different cognitive domains, acquired from the Human Connectome Project task-fMRI database. Using a 10s window of fMRI response, the 21 cognitive states were identified with a test accuracy of 90% (chance level 4.8%). Performance remained good when using a 6s window (82%).