Interview with Nathalie Regard & Roberto Toro
by AmanPreet Badhwar and Ekaterina Dobryakova
“...I wander all night in my vision,
Stepping with light feet, swiftly and noiselessly stepping and stopping,
Bending with open eyes over the shut eyes of sleepers …..
The female that loves unrequited sleeps,
And the male that loves unrequited sleeps,
The head of the money-maker that plotted all day sleeps,
And the enraged and treacherous dispositions, all, all sleep …..
I love the rich running day, but I do not desert her in whom I lay so long,
I know not how I came of you and I know not where I go with you, but
I know I came well and shall go well...”
-Walt Whitman, The Sleepers, 1892
The Mesopotamians archived their dreams on clay tablets, the Egyptians wrote down their dreams on papyrus. While many throughout history have recorded dreams, at OHBM 2017 we were provided with the opportunity to view their tangible form in 3D representations of brain activity. This art and neuroscience initiative, entitled Dream Sessions, was undertaken by professional artist Nathalie Regard in collaboration with neuroscientists Roberto Toro and Guillaume Dumas. Creative pieces from Dream Sessions (both 80 Days in Dreams and 101Nights) were showcased at this year’s OHBM art exhibit, entitled “Levels of Thought”, along with artworks from other artists and neuroscientists.
OHBM 2017 ABSTRACT HIGHLIGHT: NOVEL DISEASE PROGRESSION SCORES AND SUSCEPTIBILITY LOCI FOR ALZHEIMER’S DISEASE
BY HUGH PEMBERTON
There are over 9.9 million new cases of dementia every year worldwide, which equates to a new case every 3.2 seconds. The discovery of susceptibility loci for Alzheimer’s disease has the potential to inform research hypotheses and could, eventually, lead to novel therapies. I sat down with Marzia Antonella Scelsi from University College London to discuss her abstract on Multi-modal Imaging Disease Progression Scores as Quantitative Traits in GWAS of the ADNI Cohort. In this work, they used a mathematical model to generate an individual score for each patient based on the stage of their Alzheimer’s disease (AD) progression. Using this score, they performed a Genome Wide Association Study (GWAS) to look for any genetic markers that may be driving disease progression.
Hugh Pemberton (HP): How would you explain your study to your non-neuroscientist friends?
Marzia Antonella Scelsi (MAS): It is commonly known that AD is very complex and involves several different pathological processes occurring at different stages. There also exist several genetic influences but with only one very well known risk factor. The heritability of the disease is not well understood so better tools are required for studying the genetics of AD. The research world is currently looking at the genetics of each pathological process separately. However, this is likely to give only limited insight into AD, without reflecting the true complexity of the disease.
We want to know what drives the onset of all the symptoms together and come up with a phenotype – i.e., a measure of the disease severity – that combines information about all the different mechanisms at play during the disease course. We integrated two different signatures of AD pathology into one measure and tried to assess the stage of AD progression for each patient (Graph A, Figure 1). Subsequently, we assigned a number to each patient based on this calculated AD progression (Figure 1B). What we get from a genetic study of this score is information on multiple disease processes at the same time.
BY JEAN CHEN
Video interview conducted by Pamela Douglas and Martin Lindquist
Many working in or with MRI know about Mark Cohen’s contributions to neuroimaging. He played a critical role in developing practical echo-planar scanning, ultra-fast MRI applications, contrast-based and BOLD functional MRI and applications of linear systems analysis to increase fMRI sensitivity and resolution.
As the creator and director of the UCLA/Semel NeuroImaging Training Program Mark has pushed his students to an integrative understanding of the role of imaging in neuroscience: the use of images as hypothesis tests and the relationship between blurring, convolution, statistical error and inference from images. We spoke to Mark to find out his background and the rationale behind the neuroimaging training program.
OHBM 2017 Abstract Highlight: Deep Learning Reveals Brain Features associated with Preterm Birth and Perinatal Risk Factors
Q & A with Manuel Hinojosa-Rodriguez
BY BRENDAN E. DEPUE
Infants and children with a history of preterm birth (PB) and with perinatal risk factors (PRF) for brain injury may exhibit structural brain abnormalities. For example, they may exhibit grey matter (GM) lesions that could impair motor or cognitive functions. However, MRI identification of these potential GM abnormalities in infants and children is very challenging and not often employed in clinical practice. Researchers have therefore devised machine learning algorithms to identify such structural abnormalities. To better understand these new tools, I got together with Manuel Hinojosa-Rodriguez currently at the Universidad Nacional Autonoma de Mexico, who collaborates with the University of Southern California.