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.

Why Does the Brain Sometimes Mistake Imagination for Reality?

Post by Soumilee Chaudhuri

The takeaway

The human brain employs common neural circuits for both external perception and internal imagination, which can lead to confusion between real and imagined experiences. The authors of this study designed an experiment that intentionally blurred the line between imagination and perception to show how a brain region called the fusiform gyrus distinguishes between internal (imagined) experiences and external (perceived) ones.

What's the science?

Decades of research have shown that visual imagination activates many of the same brain regions (visual cortices) involved in actual visual perception. While this shared use of neural resources is efficient, it also creates a challenge: the brain might confuse imagined experiences with real ones. Previous research has demonstrated that when imagination is very vivid, individuals are more likely to believe what they imagine is real. The exact neural mechanisms by which the brain differentiates between externally perceived and internally generated sensory information remain unclear. This week in Neuron, Dijkstra and colleagues used brain imaging while participants viewed and imagined specific visual patterns to try and answer this question.

How did they do it?

As part of this study, twenty-six healthy volunteers took part in a visual detection task while their brain activity was recorded using functional magnetic resonance imaging (fMRI). All of them completed a behavioral training session to practice detecting and imagining oriented faint patterns or gratings. For the main task, they were shown these gratings that tilted either left or right, sometimes hidden in noisy backgrounds. At the same time, participants were asked to imagine a grating that either matched (congruent) or didn't match (incongruent) the pattern they were trying to detect, with these conditions changing in blocks. After each trial, they reported whether they thought a real grating appeared and rated the vividness of their mental image. Participants judged whether a pattern was present and rated the vividness of their mental imagery. Their brain scans were then analyzed to understand how the brain combines signals from both seeing and imagining to tell apart reality from imagination.

What did they find?

The study found that the bilateral fusiform gyrus (FG) plays a crucial role in determining whether what we see is real or imagined. Brain activity in the FG was more potent when what people imagined matched the real stimulus (called the congruent condition), especially in the left FG, where this effect was highly significant. Individuals who exhibited greater confusion between imagination and reality also showed more vigorous FG activity on the right side. Moreover, the vividness of people's mental images also influenced FG activity. In contrast, other brain areas showed some activity but lacked the specific pattern required to distinguish between reality and imagination. Significantly, activity in the left FG could predict when people mistakenly thought an imagined image was real, but only when the imagined image matched the one they were looking for (i.e., the congruent condition). This shows that the FG helps the brain distinguish between sensory signals and imagination, explaining why we sometimes confuse the two.

What's the impact?

This research reveals how the brain distinguishes real experiences from imagined ones by using the FG to combine signals from perception and mental imagery into a "reality signal” or RS. This signal helps decision-making regions of the brain determine whether something is truly happening or just imagined. Disruption in this biological process could contribute to serious dysregulation of sensory perceptions, such as hallucinations, especially associated with psychiatric illnesses such as schizophrenia. This work sets the stage for developing targeted and effective treatments for individuals who struggle to distinguish between imagination and reality in everyday life.

Ultra-Rare Variants in Mitochondrial DNA Identified in Bipolar Disorder

Post by Amanda Engstrom

The takeaway

Mitochondrial dysfunction has been thought to play a role in various neuropsychiatric disorders. In bipolar disorder, there is an enrichment of a small number of ultra-rare, potentially pathogenic mitochondrial DNA variants that could be directly targeted to improve mitochondrial function in the brain. 

What's the science?

Bipolar disorder (BD) is a major psychiatric disorder characterized by recurrent manic and depressive episodes. Despite being highly heritable, genomic studies have not been able to specify the mechanism underlying BD etiology. However, different variants, or changes to the DNA sequence, of mitochondrial DNA have been suggested to be a factor in BD. The mitochondria play a critical role in the function of all cells and have been theoretically linked to neuronal dysfunction due to their high requirement for energy processing. This week in Molecular Psychiatry, Ohtani and colleagues used advanced DNA sequencing technology to investigate the contribution of brain mitochondrial DNA variants to BD’s pathophysiology. 

How did they do it?

The authors analyzed brain DNA from 54 BD patients, 55 schizophrenia patients, and 54 healthy controls to investigate the association between BD and mitochondrial DNA variants. They used duplex molecular barcode sequencing, a high-precision technique that tags each DNA molecule to detect rare mitochondrial DNA variants with single-molecule resolution. This approach allowed for the identification of heteroplasmic variants, where both normal and mutated mitochondrial DNA are present within the same cell. They then calculated the Variant Allele Frequency (VAF), that is the percentage of DNA sequences in a sample that carry the specific genetic variant. Using both bulk analysis and single-molecule analysis pipelines, they could detect variants with either high and moderate-VAF or low VAF, respectively. The authors could then determine which variants are present and the frequency of each variant in healthy controls compared to BP or schizophrenia patients. 

The authors hypothesized two potential models for the mode of association between BD and mitochondrial variants in the brain:

1- A limited number of likely pathogenic variants with a high VAF would cause mitochondrial dysfunction and impair neuronal activity.

2- Accumulation of numerous non-specific variants with low VAF progressively degrades mitochondrial function and impairs neuronal activity. 

What did they find?

Using bulk sequencing analysis, the authors identified 116 heteroplasmic variants with high/moderate VAF. Thirty-six of them were ultra-rare variants (rarely seen in healthy controls). Both BD and schizophrenia brain mitochondrial DNA had more variants compared to controls, but when limiting the analysis to the ultra-rare variants, only BD had an increase in variants. Through further investigation of these ultra-rare variants, the authors identified potentially pathogenic mutations such as 1) the m.3243A>G mutation, which is causative of the mitochondrial disorder MELAS, 2) four loss-of-function mutations, and 3) six rRNA variants with scores for high pathogenicity. To detect low-VAF variants, the authors used single-molecule analysis. They identified a total of 52,312 low-VAF heteroplasmic variants, however, there was no difference in the frequency of these variants between BD, schizophrenia, and controls. There was an association with age across all groups, with the frequencies increasing with age, reinforcing prior evidence of age-associated systematic mutation rate increase. Additionally, when analyzing the mutation patterns at the single nucleotide level, there was no difference in the mutation pattern among the three groups. Together, these data suggest the mitochondrial variants in BD are due to a consistent specific mutagenic mechanism and not due to a general increase in DNA variability, supporting model 1 from the authors' original hypothesis. Thus, a subset of BD patients could harbor ultra-rare, potentially pathogenic, mitochondrial DNA mutations in their brain tissue, which could be impacting the pathophysiology of their disease. 

What's the impact?

This study was the first to examine mitochondrial variant accumulation at single-molecule resolution in mood disorders. Ultra-rare loss-of-function and rRNA variants are novel candidates for further investigation into the role of mitochondrial dysfunction in BD. There are already several compounds targeting mitochondrial function that could be useful therapies for the treatment of patients with BD.

Access the original scientific publication here