Reconstructing visual stimuli from human brain activity (e.g., fMRI) bridges neuroscience and computer vision by decoding neural representations. However, existing methods often overlook critical brain structure-function relationships, flattening spatial information and neglecting individual anatomical variations. To address these issues, we propose (1) a novel sphere tokenizer that explicitly models fMRI signals as spatially coherent 2D spherical data on the cortical surface; (2) integration of structural MRI (sMRI) data, enabling personalized encoding of individual anatomical variations; and (3) a positive-sample mixup strategy for efficiently leveraging multiple fMRI scans associated with the same visual stimulus. Collectively, these innovations enhance reconstruction accuracy, biological interpretability, and generalizability across individuals. Experiments demonstrate superior reconstruction performance compared to SOTA methods, highlighting the effectiveness and interpretability of our biologically informed approach.

Brain mapping analyzes the wavelengths of brain signals and outputs them in a map, which is then analyzed by a radiologist. Introducing Machine Learning (ML) into the brain mapping process reduces the variable of human error in reading such maps and increases efficiency. A key area of interest is determining the correlation between the functional areas of the brain on a voxel (3-dimensional pixel) wise basis. This leads to determining how a brain is functioning and can be used to detect diseases, disabilities, and sicknesses. As such, random noise presents a challenge in consistently determining the actual signals from the scan. This paper discusses how an algorithm created by Random Matrix Theory (RMT) can be used as a tool for ML, as it detects the correlation of the functional areas of the brain. Random matrices are simulated to represent the voxel signal intensity strength for each time interval where a stimulus is presented in an fMRI scan. Using the Marchenko-Pastur law for Wishart Matrices, a result of RMT, it was found that no matter what type of noise was added to the random matrices, the observed eigenvalue distribution of the Wishart Matrices would converge to the theoretical distribution. This means that RMT is robust and has a high test-re-test reliability. These results further indicate that a strong correlation exists between the eigenvalues, and hence the functional regions of the brain. Any eigenvalue that differs significantly from those predicted from RMT may indicate the discovery of a new discrete brain network.

Second row: images reconstructed by AI based on brain recordings from a macaque. Artificial intelligence systems can now create remarkably accurate reconstructions of what someone is looking at based on recordings of their brain activity. These reconstructed images are greatly improved when the AI learns which parts of the brain to pay attention to. "As far as I know, these are the closest, most accurate reconstructions," says Umut Güçlü at Radboud University in the Netherlands. How this moment for AI will change society forever (and how it won't) Güçlü's team is one of several around the world using AI systems to work out what animals or people are seeing from brain recordings and scans. In one previous study, his team used a functional MRI (fMRI) scanner to record the brain activity of three people as they were shown a series of photographs.

Deep learning analyses have offered sensitivity leaps in detection of cognitive states from functional MRI (fMRI) measurements across the brain. Yet, as deep models perform hierarchical nonlinear transformations on their input, interpreting the association between brain responses and cognitive states is challenging. Among common explanation approaches for deep fMRI classifiers, attribution methods show poor specificity and perturbation methods show limited plausibility. While counterfactual generation promises to address these limitations, previous methods use variational or adversarial priors that yield suboptimal sample fidelity. Here, we introduce the first diffusion-driven counterfactual method, DreaMR, to enable fMRI interpretation with high specificity, plausibility and fidelity. DreaMR performs diffusion-based resampling of an input fMRI sample to alter the decision of a downstream classifier, and then computes the minimal difference between the original and counterfactual samples for explanation. Unlike conventional diffusion methods, DreaMR leverages a novel fractional multi-phase-distilled diffusion prior to improve sampling efficiency without compromising fidelity, and it employs a transformer architecture to account for long-range spatiotemporal context in fMRI scans. Comprehensive experiments on neuroimaging datasets demonstrate the superior specificity, fidelity and efficiency of DreaMR in sample generation over state-of-the-art counterfactual methods for fMRI interpretation.

We present TFF, which is a Transformer framework for the analysis of functional Magnetic Resonance Imaging (fMRI) data. TFF employs a two-phase training approach. First, self-supervised training is applied to a collection of fMRI scans, where the model is trained to reconstruct 3D volume data. Second, the pre-trained model is fine-tuned on specific tasks, utilizing ground truth labels. Our results show state-of-the-art performance on a variety of fMRI tasks, including age and gender prediction, as well as schizophrenia recognition. Our code for the training, network architecture, and results is attached as supplementary material.

Scientists have developed an artificial intelligence (AI) that can figure out what you are looking at, just by monitoring your brain activity. The research is another step towards a direct'telepathic' connection between brains and computers, and could one day lead to decoding of video, imagined pictures or even dreams. A preprint of the work, performed by computer scientists at the Chinese Academy of Sciences, has been uploaded to the arXiv server, though is not yet been peer-reviewed. Functional magnetic resonance imaging (fMRI) has provided a window on the brain – a way to monitor what areas of the brain are active at any given moment. Since the 1990s the technique has helped spark a revolution in brain research.

AI algorithms can still come loaded with racial bias, even if they're trained on data more representative of different ethnic groups, according to new research. An international team of researchers analyzed how accurate algorithms were at predicting various cognitive behaviors and health measurements from brain fMRI scans, such as memory, mood, and even grip strength. Medical datasets are often skewed – they're not collected from a diverse enough sample size, and certain groups of the population are left out or misrepresented. It's not surprising if predictive models that try to detect skin cancer, for example, aren't as effective when analyzing darker skin tones than lighter ones. Biased datasets are often the source for why AI models are also biased.

Here are the most tweeted papers that were uploaded onto arXiv during September 2020. Results are powered by Arxiv Sanity Preserver. Abstract: Hardware, systems and algorithms research communities have historically had different incentive structures and fluctuating motivation to engage with each other explicitly. This historical treatment is odd given that hardware and software have frequently determined which research ideas succeed (and fail). This essay introduces the term hardware lottery to describe when a research idea wins because it is suited to the available software and hardware and not because the idea is superior to alternative research directions.

Last month, Elon Musk's Neuralink demonstrated that it is possible to monitor brain activity from our phones. There were speculations around Neuralink of what potential it has for the future generations. Decoding brain signals has great implications in medicine. A disabled person can be assisted, can understand what a speechless person is feeling and more. So, can we know what someone is thinking?

Over the last twenty five years, advances in the collection and analysis of fMRI data have enabled new insights into the brain basis of human health and disease. Individual behavioral variation can now be visualized at a neural level as patterns of connectivity among brain regions. Functional brain imaging is enhancing our understanding of clinical psychiatric disorders by revealing ties between regional and network abnormalities and psychiatric symptoms. Initial success in this arena has recently motivated collection of larger datasets which are needed to leverage fMRI to generate brain-based biomarkers to support development of precision medicines. Despite methodological advances and enhanced computational power, evaluating the quality of fMRI scans remains a critical step in the analytical framework. Before analysis can be performed, expert reviewers visually inspect raw scans and preprocessed derivatives to determine viability of the data. This Quality Control (QC) process is labor intensive, and the inability to automate at large scale has proven to be a limiting factor in clinical neuroscience fMRI research. We present a novel method for automating the QC of fMRI scans. We train machine learning classifiers using features derived from brain MR images to predict the "quality" of those images, based on the ground truth of an expert's opinion. We emphasize the importance of these classifiers' ability to generalize their predictions across data from different studies. To address this, we propose a novel approach entitled "FMRI preprocessing Log mining for Automated, Generalizable Quality Control" (FLAG-QC), in which features derived from mining runtime logs are used to train the classifier. We show that classifiers trained on FLAG-QC features perform much better (AUC=0.79) than previously proposed feature sets (AUC=0.56) when testing their ability to generalize across studies.