Using New Technology to Classify Migraines

Post by Anastasia Sares

The takeaway

This study shows two exciting new technologies (functional near-infrared spectroscopy and machine learning) being put to use for the eventual better diagnosis of migraines.

What's the science?

Migraines are debilitating health episodes that include symptoms like nausea, painful headaches, fatigue, and light or sound sensitivity. It is relatively common, affecting over 1 in 10 people, with women three times more likely to suffer migraines than men. For some people, migraines also come along with an aura—a neurological abnormality like distorted vision.

Having migraines with auras is a risk factor for other conditions like stroke and heart attack, so it is important to identify them early. However, migraine diagnosis is not based on an objective test, but by a questionnaire filled out by the patient. This has two problems: first, people are not great at remembering all of their symptoms while sitting in the doctor’s office filling out a form, and second, doctors have limited time to tease out these symptoms during an appointment. 

This week in Biophotonics, Gulay and colleagues used a relatively new neuro-imaging technology, functional Near-Infrared Spectroscopy (fNIRS), combined with machine learning to classify migraine patients with and without aura, as well as no-migraine control participants.

How did they do it?

The authors performed fNIRS scanning on 32 participants, eight of whom had migraines with aura, twelve of whom had migraines without aura, and twelve of whom had no migraines at all. The participants sat for a 20-second rest period followed by a 3-minute Stroop task while an fNIRS machine recorded data. The Stroop task is an executive function task where participants are required to use inhibition when presented with a word (e.g., "red") printed in a different color (e.g., blue) and asked to name the color of the ink while ignoring the written word. fNIRS data is collected with a headband-like device containing tiny bulbs that shine light towards the scalp, where it scatters, some light penetrating deeper and some shallower. The headband is also equipped with sensors, which pick up the scattered light and analyze it. The light was limited to two very specific wavelengths that can be absorbed by molecules in the blood that carry oxygen (hemoglobin). In this way, fNIRS can track oxygen-rich and oxygen-poor blood as it flows in the brain just below the skull.

Once the data were gathered, the authors performed many mathematical operations on the signal to determine its characteristics: variance, entropy, and power over time, among many others. They then fed these values into a machine learning algorithm, training it to classify between the three groups of participants.

What did they find?

The model’s classification accuracy was evaluated by the leave-one-out method, in which the model is trained on all participants but one, and then asked to classify the final participant as a test. This is repeated many times with a random participant left out each time to obtain an accuracy score. The author’s model had an overall balanced accuracy of 84% to detect migraines with aura, 98% accuracy to detect migraines without aura, and 95% accuracy to detect people without migraines at all. Classification was best when using data from the left prefrontal cortex.

What's the impact?

This work shows the potential of a 5-minute neuroimaging protocol to detect migraines with aura, allowing for clinical follow-up. fNIRS is also more practical because, unlike MRI and EEG, it is less disrupted by a person’s movements; it is also less expensive than MRI and can be less time-consuming than EEG to set up.

Access the original scientific publication here.

How The Brain Recovers From Sleep Debt

Post by Natalia Ladyka-Wojcik 

The takeaway

After a period of sleep deprivation, our bodies settle the (sleep) score by entering into a period of persistent and deep recovery sleep. For the first time, scientists have discovered the neural circuit that promotes recovery sleep, providing key insights into how the brain maintains sleep homeostasis. 

What's the science?

Sleep is governed by homeostatic control, the body’s mechanism for maintaining a stable internal environment despite changes in the external environment. When we experience sleep deprivation, the resulting accumulation of “sleep debt” prompts the body to restore sleep balance by initiating a period of persistent and deep recovery sleep. Although many molecular and cellular mechanisms have been proposed to regulate sleep, we still don’t know what specific neural circuits may detect or transmit homeostatic signals to sleep-promoting brain regions. This week in Science, Lee and colleagues set out to identify a neural circuit responsible for triggering this essential recovery sleep, using tools that allow neuroscientists to control the signaling of brain cells in mice.  

How did they do it?

In mammals, sleep can be categorized into two types: rapid eye movement (REM) sleep and non-REM sleep, the latter of which is considered a deeper, recovery-type sleep. Here, the authors mapped a group of excitatory neurons in the thalamus of mice that project to brain regions which are thought to promote non-REM sleep. Specifically, they investigated non-REM, homeostatic recovery sleep after activating and inhibiting neurons in the nucleus reuniens of the thalamus – a major relay station for sensory and motor information in the brain. The authors used a technique called chemogenetics to inhibit neurons of the nucleus reuniens during sleep deprivation in order to determine if subsequent non-REM recovery sleep would be affected. A similar approach using optogenetics, a tool that uses targeted pulses of light to control the activation of neurons, was also used to determine if the stimulation of excitatory neurons in the nucleus reuniens would promote sleep behaviors. Finally, the authors assessed the downstream impact of activation in these neurons by tracing their projections to other non-REM sleep-promoting brain regions.

What did they find?

The authors found that inhibiting neurons in the thalamic nucleus reuniens decreased the quality of homeostatic, non-REM recovery sleep that the mice subsequently experienced. In contrast, stimulated neurons in the nucleus reuniens led to mice exhibiting longer, deeper, non-REM sleep after a delay, suggesting that these neurons regulate sleep homeostasis. The authors also found that mice engaged in more behaviors associated with preparation for sleep, such as self-grooming, after optogenetic activation of these neurons. Importantly, after longer periods of sleep deprivation, neurons in the nucleus reuniens fired more frequently while the mice were awake – an effect that diminished with subsequent recovery sleep. Finally, the authors found that these neurons projected to a small subthalamic region called the zona incerta, to generate non-REM recovery sleep. Curiously, sleep deprivation enhanced interactions between the nucleus reuniens and zona incerta, whereas disrupting synaptic plasticity in the nucleus reuniens impaired this interaction and reduced non-REM sleep.

What's the impact?

This study is the first to identify a neural circuit responsible for homeostatic control over non-REM recovery sleep, separate from regular sleep-wake cycles. Specifically, these findings suggest that during sleep deprivation, brain regions that promote non-REM sleep increase their communication to drive deeper, more restorative sleep. By uncovering the brain mechanisms that support recovery sleep in mice, this research provides insight into what may happen in the human brain after sleep loss, particularly in conditions like idiopathic hypersomnia, where patients experience an overwhelming and persistent need for sleep.

Access the original scientific publication here.

How Cognitive Fatigue Affects Effort-Based Choices

Post by Meagan Marks

The takeaway

Cognitive fatigue—that well-known feeling after a long day of work—typically reduces our motivation to take on additional tasks. During this decline in motivation, the dorsolateral prefrontal cortex and right insula exhibit strengthened connectivity, providing insight into the neurobiology of fatigue and suggesting a potential target for amotivation.

What's the science?

Cognitive fatigue is a familiar feeling that follows sustained mental effort, building up throughout the workday and reducing our willingness to engage in further exertion. Despite its relevance, the mechanism by which cognitive fatigue is generated in the brain and its influence on decision-making circuitry remain unclear. Understanding the neurobiology behind cognitive fatigue and its impact on exertion-related choices will not only offer insight into everyday brain function but may also help identify neural networks involved in amotivation—a lack of motivation and energy that often accompanies many psychiatric and neurological conditions. This week in the Journal of Neuroscience, Steward and colleagues identify brain regions involved in cognitive fatigue and examine how they interact with effort-based decision-making areas to uncover how fatigue shapes effort-based choices.

How did they do it?

The study involved 28 participants (18 females, 10 males), who first practiced the experimental task—a version of the “n-back” memory task—outside of the magnetic resonance imaging (MRI) scanner. In this task, participants were shown a sequence of letters, one at a time, and were periodically asked whether a letter matched one presented “n” letters earlier, with “n” ranging from 1 to 6. Higher values of “n” represented greater cognitive effort, and each effort level was paired with a specific color (e.g., n=1 in green to represent minimal effort, n=6 in blue to represent maximum effort), allowing participants to associate each color with a corresponding level of mental exertion.

After this association phase, participants entered the scanner. To establish a baseline, they completed 80 trials in which they repeatedly chose between a simple n=1 task for $1 or a more cognitively demanding n-back task (displayed by color) for a higher monetary reward. Participants then entered the experimental or ‘fatigue phase’, which followed the same structure but included intermittent bouts of mentally demanding tasks designed to induce fatigue. This phase also consisted of 80 trials.

A control group followed the same protocol, except rest periods replaced the exertion bouts during the second phase. This controlled for potential confounding factors such as time, task exposure, or trial order, ensuring that any observed effects were specifically attributed to cognitive fatigue.

What did they find?

As expected, participants were less likely to choose high-effort options when fatigued—preferring low-effort, low-reward choices—especially as the experiment progressed, compared to baseline. This effect was not seen in the control group, indicating that the behavioral changes were due to cognitive fatigue.

Neuroimaging data revealed that regions within the brain’s effort-valuation network showed altered activity based on the monetary value and perceived effort level of choices. This pattern held across both the fatigue and baseline phases. However, one effort-valuation region—the right insula—showed greater fluctuations in activity in response to the effort-based decisions during the fatigue phase. This suggests it is particularly sensitive to cognitive fatigue and may play a role in evaluating effort when mental resources are drained. During fatigue, this region also showed increased connectivity with the dorsolateral prefrontal cortex, a region associated with cognitive control and demand. Activity in the dorsolateral prefrontal cortex rose with increasing fatigue, suggesting it may help detect when the brain is fatigued. The strengthened connectivity between the right insula and dorsolateral prefrontal cortex during fatigue implies that these regions may work together to integrate information about an individual’s cognitive state and guide decisions about future mental effort.

What's the impact?

This study is the first to identify a potential circuit that modulates our effort-based choices and evaluations when mentally fatigued. Two brain regions— the dorsolateral prefrontal cortex (a ‘fatigue’ region) and the right insula (an effort-valuation region)— show strengthened communication when making effort-based decisions during a fatigued state, indicating that they may work together to influence our choice to perform additional mental exertion when in a state of cognitive fatigue. Understanding this connection not only uncovers the neurobiology behind a common human experience but also points to a possible target for addressing amotivation, a debilitating symptom in many neurological and psychiatric conditions.

Access the original scientific publication here.