Neuroimaging in Clinical Trials

Ideas and Inspiration
, ,
1

Neuroimaging techniques have become increasingly important in clinical trials for neurodegenerative diseases, particularly Alzheimer’s disease (AD) and Parkinson’s disease (PD). These advanced imaging methods offer valuable insights for diagnosis, monitoring disease progression, and evaluating treatment efficacy.

Neuroimaging modalities widely used in clinical trials include:

Magnetic Resonance Imaging (MRI): This provides high-resolution structural imaging of the brain, allowing researchers to: 1. detect early signs of atrophy in specific brain regions; 2. monitor disease progression over time; and 3. screen potential participants for clinical trials (Neuroimaging Advances in Neurologic and Neurodegenerative Diseases - PMC).

Positron Emission Tomography (PET): This technique offers visualization of specific molecular targets. For instance, amyloid PET tracers can detect β-amyloid plaques (Advancing the Frontier: Neuroimaging Techniques in the Early Detection and Management of Neurodegenerative Diseases - PMC), while tau PET tracers visualize tau protein aggregates in AD. In PD, [18F]-FDOPA PET assesses dopaminergic function (https://academic.oup.com/book/3248/chapter-abstract/144206132?redirectedFrom=fulltext&login=true). Research is ongoing for alpha-synuclein PET tracers (Closing In on a Groundbreaking Imaging Tool for Parkinson's Disease | Parkinson's Disease).

Single Photon Emission Computed Tomography (SPECT): To date, DAT- SPECT imaging, particularly using tracers like Ioflupane (123I) also known as DaTScan, is the only FDA-approved imaging method as a supplemental tool for PD diagnosis. It has been employed to evaluate dopamine transporter function in PD trials and provides objective measures of treatment outcomes (https://academic.oup.com/book/3248/chapter-abstract/144206132?redirectedFrom=fulltext&login=true).

Applications in clinical trials:

  1. Participant selection: Neuroimaging tools serve as enrichment tools by helping ensure appropriate participant inclusion by confirming diagnoses and identifying individuals at high risk for specific pathologies.
  2. Outcome measures: Imaging biomarkers serve as secondary outcome measures providing objective endpoints in clinical trials, potentially offering greater sensitivity to change than clinical assessments alone.
  3. Disease modification assessment: Neuroimaging can help differentiate between symptomatic effects and true disease-modifying properties of investigational drugs.
  4. Safety monitoring: MRI can be used to monitor for adverse effects such as amyloid-related imaging abnormalities in AD trials.

Future directions: While neuroimaging in clinical trials offers numerous advantages, several challenges remain to be addressed. These include standardization of imaging protocols across multiple sites, cost and availability of advanced imaging technologies, and integration of imaging data with other biomarkers and clinical outcomes.

2 Likes
2

Thanks for sharing @AmgadDroby! Wondering if you or @bmarebwa have any suggestions for researchers interested in integrating imaging data into a larger multimodal data analysis.

Where is a good place to start? Are there particular training resources you rely on or suggest for students? Particular data sets that you think are a good place to begin?

Also noticed that DaT scans are absent from the list above, curious to your thoughts on the benefits/challenges/differences in those vs. say, PET scans? Thanks!

2 Likes
3

Thank you for your question, @jgottesman. I have made it clearer under the SPECT subtitle that the primary imaging modality used in PD to date it DaTSCAN. It’s worthwhile mentioning that current research is also exploring the sensitivity of different tracers including alpha-Synuclein for example.

Regarding image processing techniques, most skills are either self-taught or easily acquired by following the user-friendly manuals provided by various tools such as FSL, SPM, and FreeSurfer. These manuals are readily accessible, allowing users to learn at their own pace. Additionally, there are open forums where users can seek guidance and find answers to specific questions.

For more specialized analyses, tutorials are available on various GitHub repositories and YouTube. To my knowledge, FSL and SPM also offers introductory courses for beginners, covering fundamental concepts and analyses. The schedule and locations for these courses are regularly updated at the following links:

3 Likes