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[1910.08415] Anatomically informed Bayesian spatial priors for fMRI analysis
"Existing Bayesian spatial priors for functional magnetic resonance imaging (fMRI) data correspond to stationary isotropic smoothing filters that may oversmooth at anatomical boundaries. We propose two anatomically informed Bayesian spatial models for fMRI data with local smoothing in each voxel based on a tensor field estimated from a T1-weighted anatomical image. We show that our anatomically informed Bayesian spatial models results in posterior probability maps that follow the anatomical structure."
to:NB  smoothing  neuroscience  fmri  statistics  spatial_statistics 
yesterday by cshalizi
[1909.12299] ExpertoCoder: Capturing Divergent Brain Regions Using Mixture of Regression Experts
"fMRI semantic category understanding using linguistic encoding models attempts to learn a forward mapping that relates stimuli to the corresponding brain activation. Classical encoding models use linear multivariate methods to predict brain activation (all the voxels) given the stimulus. However, these methods mainly assume multiple regions as one vast uniform region or several independent regions, ignoring connections among them. In this paper, we present a mixture of experts model for predicting brain activity patterns. Given a new stimulus, the model predicts the entire brain activation as a weighted linear combination of activation of multiple experts. We argue that each expert captures activity patterns related to a particular region of interest (ROI) in the human brain. Thus, the utility of the proposed model is twofold. It not only accurately predicts the brain activation for a given stimulus, but it also reveals the level of activation of individual brain regions. Results of our experiments highlight the importance of the proposed model for predicting brain activation. This study also helps in understanding which of the brain regions get activated together, given a certain kind of stimulus. Importantly, we suggest that the mixture of regression experts (MoRE) framework successfully combines the two principles of organization of function in the brain, namely that of specialization and integration."
to:NB  neural_data_analysis  fmri  mixture_models  statistics  ensemble_methods 
21 days ago by cshalizi
[1908.06319] Locally Linear Embedding and fMRI feature selection in psychiatric classification
"Background: Functional magnetic resonance imaging (fMRI) provides non-invasive measures of neuronal activity using an endogenous Blood Oxygenation-Level Dependent (BOLD) contrast. This article introduces a nonlinear dimensionality reduction (Locally Linear Embedding) to extract informative measures of the underlying neuronal activity from BOLD time-series. The method is validated using the Leave-One-Out-Cross-Validation (LOOCV) accuracy of classifying psychiatric diagnoses using resting-state and task-related fMRI. Methods: Locally Linear Embedding of BOLD time-series (into each voxel's respective tensor) was used to optimise feature selection. This uses Gauß' Principle of Least Constraint to conserve quantities over both space and time. This conservation was assessed using LOOCV to greedily select time points in an incremental fashion on training data that was categorised in terms of psychiatric diagnoses. Findings: The embedded fMRI gave highly diagnostic performances (> 80%) on eleven publicly-available datasets containing healthy controls and patients with either Schizophrenia, Attention-Deficit Hyperactivity Disorder (ADHD), or Autism Spectrum Disorder (ASD). Furthermore, unlike the original fMRI data before or after using Principal Component Analysis (PCA) for artefact reduction, the embedded fMRI furnished significantly better than chance classification (defined as the majority class proportion) on ten of eleven datasets. Interpretation: Locally Linear Embedding appears to be a useful feature extraction procedure that retains important information about patterns of brain activity distinguishing among psychiatric cohorts."

--- Last tag is because I plan to teach LLE and this might make a good example or assignment, if I like how it was actually done.

--- ETA: It's... not horrible (though the writing is bad and far too pretentious), but not very insightful, and too complicated to make a good teaching example.
to:NB  locally_linear_embedding  classifiers  fmri  dimension_reduction  have_read  to_teach:data-mining 
9 weeks ago by cshalizi
Sequential replay of nonspatial task states in the human hippocampus | Science
"Electrophysiological recordings in rats and mice have shown that specific hippocampal neuronal activity patterns are sequentially reactivated during rest periods or sleep. Does the human hippocampus also replay activity sequences, even in a nonspatial task, such as, for example, decision-making? Schuck and Niv studied functional magnetic resonance imaging signals in subjects after they had learned a decision-making task. While people rested, the replay of activity patterns in the hippocampus reflected the order of previous task-state sequences. Thus, sequential hippocampal reactivation might participate in decision-making in humans."
to:NB  neuroscience  fmri  memory  neural_control_of_action 
10 weeks ago by cshalizi
[1906.07265] Recovering low-rank structure from multiple networks with unknown edge distributions
"In increasingly many settings, particularly in neuroimaging, data sets consist of multiple samples from a population of networks, with vertices aligned across networks. For example, fMRI studies yield graphs whose vertices correspond to brain regions, which are the same across subjects. We consider the setting where we observe a sample of networks whose adjacency matrices have a shared low-rank expectation, but edge-level noise distributions may vary from one network to another. We show that so long as edge noise is sub-gamma distributed in each network, the shared low-rank structure can be recovered accurately using an eigenvalue truncation of a weighted network average. We also explore the extent to which edge-level errors influence estimation and downstream inference tasks. The proposed approach is illustrated on synthetic networks and on an fMRI study of schizophrenia."
to:NB  network_data_analysis  to_teach:baby-nets  levina.elizaveta  statistics  fmri 
june 2019 by cshalizi
Charles Limb: Your brain on improv | TED Talk
fMRI analyses on jazz improv and free-style hip-hop rap

When creative, brains shut off the self-monitoring areas and activate the self-expression areas.
creativity  fMRI  brain  neuro 
april 2019 by dandv
New Evidence for the Strange Geometry of Thought - Facts So Romantic - Nautilus
“[the] usefulness of a cognitive space isn’t just restricted to already familiar object comparisons. “One of the ways these cognitive spaces can benefit our behavior is when we encounter something we have never seen before,” Bellmund said. “Based on the features of the new object we can position it in our cognitive space. We can then use our old knowledge to infer how to behave in this novel situation.” Representing knowledge in this structured way allows us to make sense of how we should behave in new circumstances.”
neuroscience  cognitivescience  concepts  space  fmri  geometry  cognition 
february 2019 by danhon
Cannabidiol May Help Normalize Brain Function in Psychosis. | Psychiatry | JAMA | JAMA Network
> The study included 33 participants who were at high risk of psychosis and experiencing significant psychotic symptoms, but who were not taking antipsychotic medication, and 19 healthy controls. The individuals at risk of psychosis were randomly assigned to receive a single oral dose of CBD or placebo. All participants underwent functional magnetic resonance imaging (fMRI) while performing a verbal learning test, which engages brain regions critical to the pathophysiology of psychosis.
CBD  psychosis  fMRI 
january 2019 by porejide
Mind-reading devices can now access your thoughts and dreams using AI | New Scientist, Sep 2018
"We can now decode dreams and recreate images of faces people have seen, and everyone from Facebook to Elon Musk wants a piece of this mind reading reality"

"From an fMRI brain scan, Liu’s AI can say which of a selection of 15 different things a person was viewing when the scan was taken. For example, if someone was looking at a picture of a face, the AI can detect patterns in their scan that convince it to say “face”. Other options include birds, aeroplanes and people exercising, and the AI can call the correct category 50 per cent of the time."

Jack Gallant, UC Berkely: "When shown brain scans of someone watching a different YouTube video, the AI was able to generate a new movie of what it thought the person was viewing. The results are eerie outlines of the original, but still recognisable."

"Yukiyasu Kamitani at Japan’s Advanced Telecommunications Research Institute first showed in 2013 that it is possible to train an AI to detect the content of someone’s dreams, describing each in basic terms such as whether there was a male or female character, the objects included and details about the overall scene. Kamitani’s system has an accuracy of about 60 per cent."

"However, one big drawback of EEG is that there is so much unwanted noise to contend with. "

"The progress using AI with fMRI is causing people to rethink what EEG might be capable of."
NewScientist  AI  neuroscience  dreams  recognitioin  fMRI  EEG  ethics 
january 2019 by pierredv
Optseq Home Page
optseq2 is a tool for automatically scheduling events for rapid-presentation event-related (RPER) fMRI experiments (the schedule is the order and timing of events).
fmri  software 
october 2018 by dougleigh

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