fMRI Neurofeedback Learning Patterns are Predictive of Personal and
Clinical Traits
- URL: http://arxiv.org/abs/2112.11014v1
- Date: Tue, 21 Dec 2021 06:52:48 GMT
- Title: fMRI Neurofeedback Learning Patterns are Predictive of Personal and
Clinical Traits
- Authors: Rotem Leibovitz, Jhonathan Osin, Lior Wolf, Guy Gurevitch and Talma
Hendler
- Abstract summary: We obtain a personal signature of a person's learning progress in a self-neuromodulation task, guided by functional MRI (fMRI)
The signature is based on predicting the activity of the Amygdala in a second neurofeedback session, given a similar fMRI-derived brain state in the first session.
- Score: 62.997667081978825
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We obtain a personal signature of a person's learning progress in a
self-neuromodulation task, guided by functional MRI (fMRI). The signature is
based on predicting the activity of the Amygdala in a second neurofeedback
session, given a similar fMRI-derived brain state in the first session. The
prediction is made by a deep neural network, which is trained on the entire
training cohort of patients. This signal, which is indicative of a person's
progress in performing the task of Amygdala modulation, is aggregated across
multiple prototypical brain states and then classified by a linear classifier
to various personal and clinical indications. The predictive power of the
obtained signature is stronger than previous approaches for obtaining a
personal signature from fMRI neurofeedback and provides an indication that a
person's learning pattern may be used as a diagnostic tool. Our code has been
made available, and data would be shared, subject to ethical approvals.
Related papers
- Neural Latent Aligner: Cross-trial Alignment for Learning
Representations of Complex, Naturalistic Neural Data [0.0]
We propose a novel unsupervised learning framework, Neural Latent Aligner (NLA), to find well-constrained, behaviorally relevant neural representations of complex behaviors.
The proposed framework learns more cross-trial consistent representations than the baselines, and when visualized, the manifold reveals shared neural trajectories across trials.
arXiv Detail & Related papers (2023-08-12T02:35:24Z) - Contrastive-Signal-Dependent Plasticity: Forward-Forward Learning of
Spiking Neural Systems [73.18020682258606]
We develop a neuro-mimetic architecture, composed of spiking neuronal units, where individual layers of neurons operate in parallel.
We propose an event-based generalization of forward-forward learning, which we call contrastive-signal-dependent plasticity (CSDP)
Our experimental results on several pattern datasets demonstrate that the CSDP process works well for training a dynamic recurrent spiking network capable of both classification and reconstruction.
arXiv Detail & Related papers (2023-03-30T02:40:28Z) - Neuro-BERT: Rethinking Masked Autoencoding for Self-supervised Neurological Pretraining [24.641328814546842]
We present Neuro-BERT, a self-supervised pre-training framework of neurological signals based on masked autoencoding in the Fourier domain.
We propose a novel pre-training task dubbed Fourier Inversion Prediction (FIP), which randomly masks out a portion of the input signal and then predicts the missing information.
By evaluating our method on several benchmark datasets, we show that Neuro-BERT improves downstream neurological-related tasks by a large margin.
arXiv Detail & Related papers (2022-04-20T16:48:18Z) - Learning Personal Representations from fMRIby Predicting Neurofeedback
Performance [52.77024349608834]
We present a deep neural network method for learning a personal representation for individuals performing a self neuromodulation task, guided by functional MRI (fMRI)
The representation is learned by a self-supervised recurrent neural network, that predicts the Amygdala activity in the next fMRI frame given recent fMRI frames and is conditioned on the learned individual representation.
arXiv Detail & Related papers (2021-12-06T10:16:54Z) - Variational voxelwise rs-fMRI representation learning: Evaluation of
sex, age, and neuropsychiatric signatures [0.0]
We propose to apply non-linear representation learning to voxelwise rs-fMRI data.
Learning the non-linear representations is done using a variational autoencoder (VAE)
VAE is trained on voxelwise rs-fMRI data and performs non-linear dimensionality reduction that retains meaningful information.
arXiv Detail & Related papers (2021-08-29T05:27:32Z) - ICAM-reg: Interpretable Classification and Regression with Feature
Attribution for Mapping Neurological Phenotypes in Individual Scans [3.589107822343127]
We take advantage of recent developments in generative deep learning to develop a method for simultaneous classification, or regression, and feature attribution.
We validate our method on the tasks of Mini-Mental State Examination (MMSE) cognitive test score prediction for the Alzheimer's Disease Neuroimaging Initiative cohort.
We show that the generated FA maps can be used to explain outlier predictions and demonstrate that the inclusion of a regression module improves the disentanglement of the latent space.
arXiv Detail & Related papers (2021-03-03T17:55:14Z) - Training Binary Neural Networks through Learning with Noisy Supervision [76.26677550127656]
This paper formalizes the binarization operations over neural networks from a learning perspective.
Experimental results on benchmark datasets indicate that the proposed binarization technique attains consistent improvements over baselines.
arXiv Detail & Related papers (2020-10-10T01:59:39Z) - BiteNet: Bidirectional Temporal Encoder Network to Predict Medical
Outcomes [53.163089893876645]
We propose a novel self-attention mechanism that captures the contextual dependency and temporal relationships within a patient's healthcare journey.
An end-to-end bidirectional temporal encoder network (BiteNet) then learns representations of the patient's journeys.
We have evaluated the effectiveness of our methods on two supervised prediction and two unsupervised clustering tasks with a real-world EHR dataset.
arXiv Detail & Related papers (2020-09-24T00:42:36Z) - Detecting Parkinsonian Tremor from IMU Data Collected In-The-Wild using
Deep Multiple-Instance Learning [59.74684475991192]
Parkinson's Disease (PD) is a slowly evolving neuro-logical disease that affects about 1% of the population above 60 years old.
PD symptoms include tremor, rigidity and braykinesia.
We present a method for automatically identifying tremorous episodes related to PD, based on IMU signals captured via a smartphone device.
arXiv Detail & Related papers (2020-05-06T09:02:30Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.