Gozde Unal, a PhD student in the lab of Dr. Marom Bikson will present her defense of her research proposal on Tuesday, May 11, 2021 at 9am. A copy of her abstract is below. If you would like to attend, please contact Gozde at gunal000@citymail.cuny.edu for the Zoom meeting ID.

ADAPTIVE CURRENT-FLOW MODELS OF ECT:

EXPLAINING INDIVIDUAL STATIC IMPEDANCE, DYNAMIC IMPEDANCE, AND BRAIN CURRENT DENSITY

Abstract

Improvements in electroconvulsive therapy (ECT) outcomes have followed refinement in device electrical output and electrode montage. The physical properties of the ECT stimulus, together with those of the patient’s head, determine the impedances measured by the device and govern current delivery to the brain and ECT outcomes. However, the precise relations among physical properties of the stimulus, patient head anatomy, and patient-specific impedance to the passage of current are long-standing questions in ECT research and practice.

We developed anatomical MRI-derived models of transcranial electrical stimulation (tES) that included changes in tissue conductivity due to local electrical current flow. These “adaptive” models simulate ECT both during therapeutic stimulation using high (~1 A) current and when dynamic impedance is measured, as well as prior to stimulation when low (~1 mA) current is used to measure static impedance. We modeled two scalp layers: a superficial scalp layer with adaptive conductivity that increases with electric field up to a subject specific maximum,

(σ_SS),

and a deep scalp layer with a subject-specific fixed conductivity,

(σ_DS).

We demonstrate that variation in these scalp parameters explain clinical data on subject-specific static impedance and dynamic impedance, their imperfect correlation across subjects, their relationships to seizure threshold, and the role of head anatomy. Adaptive tES models demonstrate that current flow changes local tissue conductivity which in turn shapes current delivery to the brain in a manner not accounted for in fixed tissue conductivity models.

Our predictions that variation in individual skin properties, rather than other aspects of anatomy, largely govern the relationship between static impedance, dynamic impedance, and current delivery to the brain, are themselves subject to assumptions about tissue properties. Broadly, our novel pipeline for tES models is important in ongoing efforts to optimize devices, personalize interventions, and explain clinical findings.

Maximilian Nentwich, a PhD student in the lab of Dr. Lucas Parra will present his defense of her research proposal on Monday, May 2, 2021 at 1pm. A copy of his abstract is below. If you would like to attend, please contact Maximilian at mnentwi000@citymail.cuny.edu for the Zoom meeting ID.

NEURAL RESPONSES TO NATURALISTIC STIMULI

Maximilian Nentwich

Department of Biomedical Engineering

Mentor: Lucas C. Parra

Abstract

While experiments in neuroscience traditionally focus on well-defined static stimuli, the

results of these studies often fail to explain neural processes in naturalistic settings. Naturalistic

stimuli, like movies, allow for free eye movements and contain various complex visual, semantic

and narrative features. However, defining these features requires subjective and labor-intensive

manual annotations. Alternatively, the reliability of neural signals between brain areas or

subjects can be analyzed. This has led to the identification of brain areas that are correlated

between subjects, dependent on the narrative content of movies and the subjects attention.

Similarly, patterns of correlations between brain areas, termed ‘functional connectivity’ (FC), are

reliably activated during resting state and movie tasks.

FC has been studied extensively with fMRI, is reliable across methods and laboratories

and related to various psychiatric and demographic phenotypes. However, FC has not been

studied well in EEG. Therefore, aim 1 is to compare FC between fMRI and EEG. We

hypothesize that patterns of FC measured by fMRI and EEG are similar, and that FC in

both modalities is related to phenotypic variables. To test this, we analyzed a database of

EEG and fMRI recorded from over 1600 children and adolescents during resting state and

movie tasks. We computed FC matrices in fMRI with Pearson’s correlation, and in EEG with the

imaginary part of coherence (iCOH), a measure of phase-coupling. We then compared the

spatial patterns of FC by correlating connectivity matrices of EEG and fMRI. FC matrices of both

measures were related to phenotypes by multivariate distance matrix regression (MDMR), which

determines if differences of connectivity matrices correspond to differences in phenotypic

measures. Contrary to our hypothesis, we found that the spatial patterns of FC in EEG and fMRI

are distinct. However, FC in both modalities is related to phenotypes. We conclude that EEG

and fMRI FC reflect different neural processes.

To investigate which features of movies drive neural responses most reliably we

analyzed an additional dataset of intracranial EEG using the same movies as the FC dataset.

Movies contain several visual features, particularly temporal contrast, scene cuts, and elicit

saccades. Previous studies on fMRI and EEG suggest that motion, particularly of socially

relevant stimuli, elicits the strongest neural responses in movies. In aim 2 we test whether

motion in movies leads to neural responses in iEEG. We hypothesise that motion elicits

stronger responses than other visual features. Further, we predict that motion of

semantic objects, elicits stronger responses than other motion. We analysed the data with

a linear systems identification approach to identify the neural responses to stimuli extracted

from the movies. We could not find any significant response to optical flow, a measure of

motion. However, we found strong responses after scene cuts and saccades. Additionally,

scene cuts identified as semantic lead to different responses than scene cuts without semantic

content. We conclude that opposed to visual motion, salient novelty events drive neural

responses to movies. We further propose a recognition memory task to test whether semantic

scene cuts are better encoded in memory.