Advancing therapies for brain disorders

Human behaviour is incredibly intricate, shaped by a jigsaw of neurological forces that determine a person’s ability to communicate, move and make decisions. When something goes wrong, a stroke, for example, a multidisciplinary approach to treatment becomes essential, encompassing physical therapies, medications and psychological support.

Despite this complexity, brain research often focuses on isolated pieces of the puzzle. However, at the Behavioral and Systems Neuroscience (UBSN) lab at The Hong Kong Polytechnic University, researchers are taking a different path. Using the latest technology, they are embracing a multidisciplinary approach, confident that it is the best way to accelerate new treatments for brain disorders.

“We are committed to tackling important research questions,” says neuroscientist, Marco Pang, UBSN director.

Researchers have a diverse range of specialist technology and expertise that they are deploying to explore seven areas: brain imaging and artificial intelligence; healthy ageing; mental health; neurorehabilitation and neural repair; human development; neurolinguistics; and cognitive neuroscience of language and culture.

Stroke rehabilitation

Pang’s team focuses on optimizing therapeutic outcomes for stroke, a condition which commonly results in impaired movement, reduced strength, coordination failure, and a lack of balance, as well as a greater risk of life-threatening falls. Physiotherapy can help, but how it affects the brain is unclear, Pang says.

His team is using imaging to study changes in the brain following physiotherapy that is designed to improve a person’s ability to combine cognitive tasks, such as counting, with motor tasks, such as balancing.

The Pang team used a combination of functional MRI (fMRI) and functional near infra-red spectroscopy (fNIRS) — two non-invasive techniques that measure changes in blood oxygenation as marker of brain activity. While fMRI offers excellent spatial resolution, it has poor temporal resolution. Conversely, fNIRS provides good temporal resolution, but does not match the spatial resolution of fMRI. The combination gives a more comprehensive view of brain activity.

“This combination has allowed us to study the brain regions involved in regulating dual-tasking and to track changes in brain activity over time or following physiotherapy interventions as stroke patients engage in these tasks,” Pang says.

Preliminary findings indicate lower brain activation in the prefrontal cortex in stroke patients, which may contribute to their struggles with tasks that require both posture control and cognitive effort¹.

“We hope to use advanced neuroimaging technologies to uncover why certain interventions are more effective for stroke rehabilitation,” Pang explains. The research could lead to more personalized and targeted therapies for stroke patients.

Pang and his team are also exploring combining low-frequency repetitive transcranial magnetic stimulation (TMS) — which uses magnetic fields applied to the head to alter brain cell activity—with physiotherapy to treat arm paresis, a partial paralysis that affects up to 50% of people at three to six months after a stroke.

In a small study², they found that applying TMS to the primary motor cortex on the non-affected side of the brain before the person received physiotherapy on their affected arm, increased excitability in the affected primary motor cortex and improved arm function. The finding supports the idea that arm paresis in stroke patients is caused in part by the healthy brain hemisphere dampening activity in the damaged hemisphere, say the researchers.

Depression treatments

Other UBSN neuroscientists are using TMS in combination with MRI and fNIRS to explore new ways to optimize treatments for depression. Intermittent theta burst stimulation (iTBS), a type of TMS that uses high frequency bursts of magnetism, was approved by the US Food and Drug Administration in 2018 for severe depression that had not responded to medication.

A USBN team led by neuroscientist, Georg Kranz, is using fMRI and fNIRS to monitor the brain’s immediate response to iTBS to better understand factors, such as time of day and caffeine intake, that could influence the effectiveness of the treatment. In a single case study³, they found that the effect of iTBS on brain activity decreased over the course of a four-week treatment, and that it was affected by the person’s mood, suggesting avenues for future multidisciplinary research.

iTBS also has potential as a prognostic tool, says Kranz. “We can use TBS to stress the brain and see what the excitability reveals about whether a patient will respond to TBS treatment,” Kranz explains, “by using machine learning to analyse patients’ brain excitability, measured with simultaneous TBS/fNIRS before treatment, we can better predict how well they respond, making treatment plans more personalized and effective.” This could reduce the trial-error-process often associated with depression therapies.

According to Kranz, UBSN’s interdisciplinary approach has turbo-charged his research by providing ample opportunities for collaboration with researchers from diverse fields, including psychologists, biomedical engineers, occupational therapists, and psychiatrists.

Linguistic development

That interdisciplinary approach has also fuelled research into developmental disorders of language at UBSN.

Few aspects of child development are as significant yet as poorly understood as the brain’s ability to learn and process language. “Underdiagnosis, misdiagnosis and insufficient understanding of these problems require urgent attention,” says Caicai Zhang, a linguistic researcher at UBSN.

Zhang and her colleagues are combining neural imaging, behavioural, and machine learning methods to investigate disorders in acquiring language skills in children. Their aim is to improve diagnosis for earlier, more effective interventions.

In one study of children between five and twelve, the researchers are using machine learning to analyse memory test results in the hopes of earlier identification of children with language issues. “Brain scans revealed that these language disorders might be connected to problems in specific brain circuits involved in memory and language processing,” says Zhang.

Zhang’s team is also examining how early memory skills can predict language abilities in four-year-olds. “We want to explore neural cognitive processes as early as possible, to identify issues, predict problems and seek interventions at the earliest opportunity,” she says — a research goal that is supported by UBSN specialists in child development, neuroimaging, speech-language pathology, computational approaches, and large language artificial intelligence (AI) models.