Spontaneous neural activity observed in resting-state fMRI is characterized by complex spatio-temporal dynamics. Different measures related to local and global brain connectivity and fluctuations in low-frequency amplitudes can quantify individual aspects of these neural dynamics. Even though such measures are derived from the same functional signals, they are often evaluated separately, neglecting their interrelations and potentially reducing the analysis sensitivity. In our study, we present a fusion searchlight (FuSL) framework to combine the complementary information contained in different resting-state fMRI metrics and demonstrate how this can improve the decoding of brain states. Moreover, we show how explainable AI allows us to reconstruct the differential impact of each metric on the decoding, which additionally increases spatial specificity of searchlight analysis. In general, this framework can be adapted to combine information derived from different imaging modalities or experimental conditions, offering a versatile and interpretable tool for data fusion in neuroimaging.

Relationship columnists and pop psychologists have long claimed that men and women are wired differently, and a new study has proven them correct. Scientists developed an artificial intelligence model that was able to tell the difference between scans of men's and women's brain activity with more than 90-percent accuracy. Most of these differences are in the default mode network, striatum, and limbic network - areas involved in a wide range of processes including daydreaming, remembering the past, planning for the future, making decisions, and smelling. With these results, scientists at Stanford Medicine add a new piece to the puzzle, supporting the idea that biological sex shapes the brain. The researchers said they are optimistic that this work will help shed light on brain conditions that affect men and women differently.

That's because machine-learning algorithms need both brain signals and information about what they correspond to, paired in perfect synchrony, to learn what the signals mean. When studying inner experience, all scientists have to go on is what people say is going on inside their head, and that can be reliable. "It's not like it's directly measuring as a ground truth what people experienced," says Raphaël Millière, a lecturer in philosophy at Macquarie University in Australia. Tying brain activity to subjective experience requires facing up to the slipperiness and inexactitude of language, particularly when deployed to capture the richness of one's inner life. In order to meet that demanding brief, scientists like Millière are marrying contemporary artificial intelligence with centuries-old techniques, from philosophical interview strategies to ancient meditation practices.

For neurological disorders and diseases, functional and anatomical connectomes of the human brain can be used to better inform targeted interventions and treatment strategies. Functional magnetic resonance imaging (fMRI) is a non-invasive neuroimaging technique that captures spatio-temporal brain function through blood flow over time. FMRI can be used to study the functional connectome through the functional connectivity matrix; that is, Pearson's correlation matrix between time series from the regions of interest of an fMRI image. One approach to analysing functional connectivity is using partial least squares (PLS), a multivariate regression technique designed for high-dimensional predictor data. However, analysing functional connectivity with PLS ignores a key property of the functional connectivity matrix; namely, these matrices are positive definite. To account for this, we introduce a generalisation of PLS to Riemannian manifolds, called R-PLS, and apply it to symmetric positive definite matrices with the affine invariant geometry. We apply R-PLS to two functional imaging datasets: COBRE, which investigates functional differences between schizophrenic patients and healthy controls, and; ABIDE, which compares people with autism spectrum disorder and neurotypical controls. Using the variable importance in the projection statistic on the results of R-PLS, we identify key functional connections in each dataset that are well represented in the literature. Given the generality of R-PLS, this method has potential to open up new avenues for multi-model imaging analysis linking structural and functional connectomics.

The Roman physician Galen was among the first people to realize that the brain controlled motor responses, cognitive function, and memory. Ever since Galen, this question has propelled the field of neuroscience. Beginning with Paul Broca's work in the 1800s, brain function has been described in terms of modular separation: each region in the brain controls a unique set of behaviors, actions, and capacities. This determination was made through observation of patients suffering neurological symptoms and connecting them to localized brain injuries. For example, Broca's area (a brain region in the inferior frontal gyrus) was found to be responsible for speech fluency (Acharya and Wroten 2022), and was discovered by studying two subjects, both of whom exhibited reduced speech capacity and suffered from lesions in the same area of the brain.

Two years ago, I decided to do nothing. As a neuroscientist, I was already familiar with the evidence that mindfulness meditation, or attending to the present moment, is beneficial for stress and anxiety. So I had been meditating regularly for about a half a year, looking to enhance my practice. And although I didn't know it yet, there were already scientific studies showing that the more extreme form of "doing nothing" that I was now interested in--floating in a sensory reduction tank--could significantly reduce stress, blood pressure, and cortisol levels. And so it was my plan, in the first week of March 2020, on what would become the eve of the COVID-19 pandemic lockdowns, to enter a commercial float studio in West Los Angeles, called Float Lab.

To find out what's behind the phenomenon of super-agers, researchers conducted a study examining the brains and cognitive performances of two groups: 41 young adults between the ages of 18 and 35 and 40 older adults between the ages of 60 and 80. First, the researchers administered a series of cognitive tests, like the California Verbal Learning Test (CVLT) and the Trail Making Test (TMT). Seventeen members of the older group scored at or above the mean scores of the younger group. That is, these 17 could be considered super-agers, performing at the same level as the younger study participants. Aside from these individuals, members of the older group tended to perform less well on the cognitive tests. The default mode network is, as its name might suggest, a series of brain regions that are active by default -- when we're not engaged in a task, they tend to show higher levels of activity.

April 17, 2019 MIA Meeting: https://www.youtube.com/watch?v RxaM2... Danilo Bzdok RWTH Aachen University Algorithms to understand default brain function Abstract: One of the least expected findings from systems neuroscience is the "Default Mode Network". This macroscopical brain network has the highest metabolic consumption and the perhaps highest neuronal baseline activity. Functional processing in this network is associated with diverse human-defining psychological processes: complex social cognition, such as perspective-taking, language and moral judgment, as well as the imagination of events and places in future and past. At the same time, the default-mode network has been linked to a range of psychiatric disorders, including schizophrenia, autism and depression. Despite its anthropological significance, the (patho-)physiological function of this network remains essentially unknown.

Since the beginning of the 21st century, the size, breadth, and granularity of data in biology and medicine has grown rapidly. In the example of neuroscience, studies with thousands of subjects are becoming more common, which provide extensive phenotyping on the behavioral, neural, and genomic level with hundreds of variables. The complexity of such big data repositories offer new opportunities and pose new challenges to investigate brain, cognition, and disease. Canonical correlation analysis (CCA) is a prototypical family of methods for wrestling with and harvesting insight from such rich datasets. This doubly-multivariate tool can simultaneously consider two variable sets from different modalities to uncover essential hidden associations. Our primer discusses the rationale, promises, and pitfalls of CCA in biomedicine.

I have two children, and they are a study in contrasts: My son works at a gym designing and building rock-climbing walls; In his spare time, he climbs them. My daughter is a Ph.D. student in immunology; In her spare time, she writes novels. My son is the sort of person you want around in a crisis, cool-headed and springing to action. Let's just say my daughter is not. My son spends money as soon as he earns it.