Thursday, May 18, 2017, 12:20PM, NAC 6/113

Basilis Gidas (Brown University), Finding Genes and Towards a Mathematical Framework for Artificial Intelligence and Biological Systems 

The first half of the lecture will be on a statistical model for finding genes in the human genome. The model contains two parts: (a) A finite network (graph) which represents the overall architecture of a gene. The vertices in the network represent DNA signals (small patterns) associated with a gene and which are recognized by proteins and enzymes involved in the transcription and translation of genes. The edges of the network correspond to interactions among these signals and represent statistical variability in the architecture across genes; (b) each signal and each part of a gene is a piece of DNA with a random length as well as a random variability of its nucleotide sequence. The second part of the model articulates these variabilities.

The above gene finding procedure is conceptually similar to what is believed to underlie speech recognition whereby recognition involves two types of information: The acoustic signal represented by a concatenation of phonemes, and global regularities articulated by grammars (or syntax). The underpinning process in visual recognition is undoubtedly similar. And so is – many practitioners believe – the functioning of biological processes whereby two principles are at work: physics (biochemistry) and evolution. Physics controls the biochemical interaction of macromolecules, but it is evolution that produced the perfect “code” or “syntactic language” for the collective behavior of genes (Gene Regulatory Networks), or the collective behavior of proteins in Signal Transduction Pathways in cell growth, cell division or immunology. While specific questions and application in speech, vision, and biology have seen impressive advances and have lead to a great deal of mathematical innovation (e.g. modern statistical learning), an underpinning mathematical framework is missing. Though we do not have the framework, we know quite a bit of some of the problems the framework needs to articulate and some of the properties it needs to have. Building on the gene finding process, the second part of the talk will aim at identifying some key sources that makes information processing in cognition and biology difficult, and hint towards a coherent hierarchical/grammatical framework.

The CCNY Neural Engineering group is excited for two important papers on the mechanisms of tDCS published in the same issue of Brain Stimulation journal.

Direct Current Stimulation Modulates LTP and LTD: Activity Dependence and Dendritic Effects.  

Kronberg G, Bridi M, Abel T, Bikson M, Parra LC.
Brain Stimul. 2017 Jan – Feb;10(1):51-58. doi: 10.1016/j.brs.2016.10.001. Epub 2016 Oct 5. PMID: 28104085

Download PDF: Kronberg_DCS


Direct Current Stimulation Alters Neuronal Input/Output Function.

Lafon B, Rahman A, Bikson M, Parra LC.
Brain Stimul. 2017 Jan – Feb;10(1):36-45. doi: 10.1016/j.brs.2016.08.014. Epub 2016 Sep 1.PMID: 27717601

Download PDF: Lafon_DCS

The SF Giants Are Zapping Their Brains With Electricity. Will It Help? MAY 8, 2017


“People like to say that electricity is the currency of the brain and that in many ways the brain is a circuit,” says Marom Bikson, a professor of biomedical engineering at City College of New York. “So when we apply electricity to the brain, we interact with that circuit, and we can change how that circuit works.”

Jackson MP, Truong D, Brownlow ML, Wagner JA, McKinley RA, Bikson M, Jankord R. Safety parameter considerations of anodal transcranial Direct Current Stimulation in rats.  Brain, Behavior, and Immunity 2017 pii: S0889-1591(17)30110-1. doi: 10.1016/j.bbi.2017.04.008 Jankord_Safety_tDCS_2017

Nitsche M. Bikson M. Extending the parameter range for tDCS: Safety and tolerability of 4 mA stimulation. Brain Stimulation. Editorial, Volume 10, Issue 3, Pages 541–542, 2017 Nitche_Bikson_BrainStim_2017

And don’t forget our seminal 2016 safety review here

Renowned neural engineer Risto Ilmoniemi will be speaking on Friday 4/21 at 3 pm in CDI 3rd floor conference room (3.352)

Professor Risto Ilmoniemi is a physicist and neuroscientist at Aalto University, Finland; he is the Head of the Department of Neuroscience and Biomedical Engineering. He built and designed multichannel MEG instruments in the 1980’s and invented for MEG use the minimum-norm estimate (together with Matti Hämäläinen), the signal-space projection, formulas for the forward problem, the channel-capacity measure for comparing sensor arrays, the triangle phantom, and several TMS techniques. He is the founder and former CEO of Nexstim Ltd., a company where he introduced navigated transcranial magnetic stimulation (TMS) and the combined use of TMS and EEG. As a professor of Applied Physics since 2006, he has led the development of new technologies for MEG–MRI and for a new generation of TMS. He is currently the Chair of the Biodesign Finland Program, which started in 2016.

See the complete Details here: Download Full Bio and Research Focus

April 7, 2017. 11:00 am to 12:30 pm, 333 Curry Student Center, Northeastern University, Boston

“Translational Neural Engineering: Accelerated medical device design for treatment of neuro-psychiatric disorders and brain injury”

The design and clinical deployment of new medical devices on an accelerated time scale (as little at 6 months) requires an interdisciplinary team and skill set spanning basic science, biomedical engineering, regulatory, and clinical trials. This talk uses a series of case-studies to diagram a process for rapid translational medical device design, with a focus on non-invasive electrical stimulation technology. This generalizable medical design process is translational because basic science stages are already informed by regulatory and clinical challenges, while clinical trials are designed around engineering features and limitations.

Slides: BiksonDesign

Prof. Luca Parra (CCNY Biomedical Engineering), On Brainwaves and Videos and Video Games
Thursday, February 09, 2017, 03:30 PM, NAC 4/156
What are the immediate neural response of the brain to natural stimuli, in particular audiovisual narratives and video games? To answer this question we record EEG while subjects are exposed to the identical audiovisual narratives and measure inter-subject correlation, which captures how similarly and reliably different people respond to the same natural stimulus. We find that inter-subject correlation of EEG is strongly modulated by attention, correlates with long term memory, and provides a quantitative estimate for “audience engagement”. In children and adolescents watching videos we find changes with age and gender that are consistent with an increase in diversity of brain responses as they mature. During video game play, which are unique experiences that preclude correlation across subjects, we measure the strength of stimulus-response correlations instead. We found that correlation with both auditory and visual responses drive the correlation observed between subjects for video and that they are are modulated by attention in video game play. Importantly, the strongest response to visual and auditory features had nearly identical neural origin suggesting that the dominant response of the brain to natural stimuli is supramodal.

Congrats on Yu (Andy) Huang, Marom Bikson, and Lucas Parra’s paper on TES model validation accepted to be published on eLife. Also thank Anli Liu’s team from NYU School of Medicine for all the experimental recordings.

Measurements and models of electric fields in the in vivo human brain during transcranial electric stimulation

Here is the link to the LINK, and a summary video.

OR Download the PDF here: e18834-download (3)  and the associated Commentary here: e25812-download