Using technology to enhance diversity and inclusivity in neuroscience and neuroimaging

How would you deal with patients inside an MRI scanner such as an aging one with hearing loss who cannot use a hearing aid or one with ALS or Parkinson’s who cannot speak clearly? Likewise, how would you make your lab, seminars, workshops, scanner facility, classrooms and conferences accessible for anyone who has hearing loss, or is neurodivergent, or does not speak your native language? Fortunately, the answer is literally right on your fingertips thanks to the advent of automatic speech recognition (ASR) apps for speech-to-text (S2T) transcriptions of any speech signal received by a smartphone, tablet or computer. Usage for actual and hypothetical scenarios will be described along with a few live demonstrations. Key to effective usage are i) clear speech, ii) audibility, and iii) stable internet connectivity.  

Although a majority of the brain is devoted to visual processing, neuroimaging reveals that blind individuals exhibit cortical plasticity, where the otherwise unused visual cortex is recruited during non-visual tasks, such as when reading braille. Science has long demonstrated that much of what we view as inherently visual in nature can just as effectively be re-imagined and achieved through alternative senses. Despite this, leaders with disabilities (including those who are blind or who have low vision) remain significantly under-represented within the science ecosystem. Barriers encountered by researchers and trainees with disabilities include those related to accessibility (such as inaccessible journal platforms and data analysis software) and those related to ableism (such as the tendency to portray people with disabilities as patients and recipients of care). This presentation will highlight key themes related to accessibility and inclusion for researchers and trainees with vision impairments in the science ecosystem, focusing on the role of inclusive technologies and digital practices based upon universal design and tangible actions that sighted faculty, researchers and practitioners can take to enhance accessibility and inclusion within classrooms, clinics and research. 

Typical EEG systems, the standard of care for neurological monitoring and a popular modality for human psychological and neurosciences, do not work well for individuals with the coarse, dense, and curly hair common in the Black population (Etienne et al., 2020; IEEE EMBC). With more than 1 billion individuals of African descent across the globe, this not only compromises neurological care for a significant portion of the population, but also excludes these groups from basic neuroscience research studies. Our team developed the first solution to this problem by creating Sèvo Systems, a simple yet effective set of devices that leverage the strength of braided hair to improve scalp contact during brain recordings in individuals with coarse, dense, and curly hair. In this talk, we will briefly describe the Sevo system and outline our ongoing assessments of its effectiveness in both research and clinical settings. Our work is the first step towards mitigating phenotypic biases embedded in this popular technology that may lead to misunderstandings of brain science and the exclusion of marginalized groups in human neuroscience and psychology research. We will also speak to other examples of phenotypic bias in neurotechnologies that we are seeking to improve at Carnegie Mellon including functional near-infrared spectroscopy (fNIRS) and pulse oximetry. We will outline ways that neuroscientists can join the cause and use equitable and inclusive methodologies based on published work (Webb et al, 2022; Nature Neuro) and our personal experiences in preparing different hair textures for neuroscience research. 

In many countries, clinical and scientific neuroimaging facilities are primarily available only in metropolitan areas. In sparsely populated countries like Australia, or countries with low socioeconomic capacity to invest in significant imaging infrastructure such as countries in Africa, this leads to significant disparities in access to clinical diagnostic imaging and opportunity to participate in scientific research. For example, a single government health service in South Australia, which spans 1 million square kilometres with 470,000 individuals, there are only 12 centres with neuroimaging (CT) facilities. This contributes to increased health costs to specific populations (e.g., remote Aboriginal and Torres Strait Islander people), and systematic biases in participant pools in scientific research. Another example is the low number of MRI scanners in the countries of the West African sub-region. In this region, the ratio of available MRI scanners per million population is a mere 0.3 in Nigeria, and 0.48 in Ghana, with countries such as Sierra Leone and Liberia having 0 scanners [1].

Portable point of care medical devices therefore will play a key role in the way that people receive medical imaging in the future. Low-cost point of care MRI scanners is one such technology that will expand the accessibility of MRI scans and provide timely diagnosis for people in remote areas. These scanners employ low (<0.2T) magnetic field strength, which provides a signal to noise ratio that is approximately 2% of that of 3T MRI scanners. As such, significant technical work is required to bring the image quality to a standard commensurate for clinical and scientific utility. Artificial intelligence (AI) based image reconstruction and post-processing methods represent a significant opportunity to address this problem.

In this presentation, I will provide an introduction to portable MRI technology, and the potential of AI post-processing to optimise image quality and SNR of low field MR images. These advances represent an important opportunity to address some of the systematic disparities in health care and research, faced by people living in regions with poor access to imaging facilities.