Brain activity patterns can predict reading difficulties

It is possible to read while using a wide range of cognitive abilities. According to a recent study, people’s reading ability can be predicted based on their connections to these areas. According to a surprising discovery made by new research, the connections formed in the brain while performing mental math calculations are also predictive of reading ability.

Chris McNorgan is a faculty member at the University of Buffalo in New York. Researchers in cognitive neuroscience are interested in how different brain regions interact when we perform various tasks. When he returned, he looked into how the brain connects when we read. For him, reading requires “making the connection between what you see on the paper and what you hear in your head,” he explains. According to him, there must be a connection between vision and hearing. It should be noted that not everyone is capable of forging these connections on their own.

Those who have dyslexia, for example, frequently struggle with reading. Because of this learning deficit, the brain has difficulty making the connection between written letters and numbers and the sounds they denote. This is in no way indicative of intelligence. As a result, even the most intelligent individuals can become entangled in words or misspellings when suffering from it. McNorgan was intrigued by the underlying neural connections that had resulted in this situation.

It is difficult to create connections between different parts of the brain. McNorgan’s brain is constantly buzzing with activity, as he describes it. As different brain parts work together, short-term connections are formed between them. Connecting the dots is something that can happen when you think critically. They connect with the content of what they are reading when they are reading it with someone else. Some people begin to form while listening to an instructor or watching a video.

Various connections are formed and broken as we mentally shift from one thing to another. McNorgan made use of computers to track down the missing links. They used artificial intelligence systems, also known as machine learning models. Training them requires a significant amount of data. To determine which characteristics of the data indicate the presence or absence of something noteworthy is the task of the models. In this study, McNorgan discovered which links were predictive of reading ability to accomplish his objective.

A model developed by McNorgan was trained using data collected from another lab. While lying in an MRI machine, the participants were asked to read a true or false word. The equipment was scanning their brains while they were reading. McNorgan used test scores to determine who was the most robust and weakest reader among the group. This model was trained using the brain scans of the students who participated in the study.

As a result, the brains of strong readers form a single set of connections. When compared to healthy individuals, those who have dyslexia have a different group of brain cells in their brain hemisphere. Reading with their left brains was something that strong reader did. In his explanation, McNorgan explains that this is the aspect of language he refers to.

“We don’t know why,” he explains, but those who struggled with the material were more likely to use the right side of their brain than the left. This side of the brain, according to McNorgan, may be used by poor readers to adjust — “to help them catch up.”

A model that works for one set of data but does not work for another is illustrated by McNorgan. This could imply that any signal observed in the training data is the result of noise rather than a genuine signal. Researchers test machine-learning models on new datasets after training them on previous datasets. This is the reason for this practice.

A second magnetic resonance imaging investigation was conducted to confirm McNorgan’s hypothesis. The pupils were required to complete simple multiplication problems independently in one phase of the test. Also required were specific reading exercises for the group members. Using this group of children, McNorgan tested his model to see if it could distinguish between outstanding readers and weak readers based on their reading skills.

His experiment included testing the model with scans collected when students were multiplying numbers rather than reading, which he described as “a novel approach.” Not expecting anything to come up, he set the model to search. In contrast, math and reading are two fundamentally different abilities that must be learned separately. Nevertheless, much to his surprise, the model accurately distinguished between solid and poor readers.

He notes that “whatever is happening in that reading network is also showing up while they’re conducting another activity,” according to McNorgan. His observations include that it is difficult to determine the precise differences between good and poor readers in their mental multiplication abilities.

It is a mistake to believe that someone will be proficient in reading and math just because their brains use some of the same connections. According to him, being able to read well and doing well in arithmetic are unrelated. “It is possible to be a lousy reader while also a great mathematician,” says the professor. Ms. McNorgan hopes that her findings may help to shed light on the mystery of why some students struggle in both reading and arithmetic, as she did in her previous research.

In the words of Marc Joanisse, “This study represents a significant step forward in our knowledge of differences in the brains of children who have learning difficulties.” There was no involvement in the research from psychologist Joanisse from the University of Western Ontario in Canada. According to him, the number of variables in learning challenges is much more significant than previously believed.

Joanisse believes that the technique has a lot of promise. This tool can be beneficial in learning more about the “many diverse features of how the brain functions.” As a result, he argues, it may be feasible to pinpoint the precise location of the source of a person’s cognitive difficulties.

Leave a Comment