Intra-regional brain dynamics linked to person-specific characteristics

People can think, behave and function very differently. These observed differences are known to be the result of complex interactions between genetics, neurobiological processes and life experiences.

Understanding the factors underlying individual differences in behavior, cognition and mental health is a key objective of numerous psychology and behavioral science studies. One approach to explore these factors entails examining patterns of brain activity that spontaneously emerge when individuals are awake but not engaged in any tasks.

Earlier research aimed at uncovering individual-specific brain activity patterns has primarily looked at the neural fluctuations indicating communication or coupling between distant brain regions. In contrast, very few studies have focused on intra-regional neural dynamics (i.e., fluctuations that take place within individual brain regions over time).

Researchers at Beijing Normal University, the Chinese Academy of Sciences and other institutes recently carried out a study aimed at uncovering intra-regional neural dynamics that could capture aspects of behavior or cognition that are unique to each person.

Their paper, published in Nature Human Behaviour, identifies specific intra-region brain activity patterns that predict substance-use tendencies and others that predict general cognitive ability.

“Spontaneous brain activity is fundamental to understanding the neural basis of inter-individual differences, making its characterization central to brain-wide association studies,” wrote Xiaohan Tian, Yingjie Peng and their colleagues in the paper.

“While inter-regional coupling patterns have been extensively studied, intra-regional dynamics remain largely unexplored. Analyzing data from four neuroimaging cohorts, we extracted ~5,000 time-series features from resting-state hemodynamic signals across 271 brain regions, offering a comprehensive characterization of intra-regional dynamics.”

Extracting neural barcodes from over 30,000 brains

As part of their study, Tian, Peng and their colleagues analyzed brain scans from four large and publicly available datasets, which were collected while individuals were awake but in a resting state. Collectively, they analyzed the brain scans of 30,148 people aged between 8 and 82.

They divided the brain into 271 regions and then used feature-extraction tools to derive descriptors of how each region’s activity changed over time. Ultimately, they were able to identify features that remained stable in the brains of each individual, forming what they call a ‘neural barcode.”

“We identified a reliable subset that serves as an individual-specific ‘barcode,” capturing multifaceted dynamic dimensions that stably reflect inter-individual variation across datasets,” wrote the authors. “These barcodes linked nonlinear autocorrelations in unimodal regions to substance use traits and random walk dynamics in higher-order networks to general cognitive abilities. Importantly, these brain–behavior associations generalized across life stages and populations, with substance use showing age-specific variation and cognition exhibiting consistent patterns across age groups.”

Interestingly, the researchers found that brain signals following specific nonlinear patterns in brain regions processing sensory signals were predictors of substance-use tendencies. In addition, slow and gradually changing signals in brain regions supporting decision-making, reasoning and memory were found to be linked to better overall mental abilities.

The link between neural ‘barcodes,’ behavior and cognition

The recent work by these researchers shows that activity patterns within individual brain regions can be indicators of people’s behavioral tendencies and traits. In the future, other neuroscientists and behavioral scientists could build on the team’s findings and conduct other studies using similar methods.

These efforts could offer valuable and generalizable insight into the biological underpinnings of person-specific cognitive abilities, traits and behavior. This insight could in turn prove useful for assessing people’s vulnerability to specific mental health disorders or for designing interventions aimed at changing unhelpful behavioral patterns.

“This work advances large-scale, generalizable brain-wide association studies by highlighting the potential of intra-regional dynamics,” wrote the authors.

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