Cognitive and Emotional Mapping With SEEG

Abstract

Mapping of cortical functions is critical for the best clinical care of patients undergoing epilepsy and tumor surgery, but also to better understand human brain function and connectivity. The purpose of this review is to explore existing and potential means of mapping higher cortical functions, including stimulation mapping, passive mapping, and connectivity analyses. We examine the history of mapping, differences between subdural and stereoelectroencephalographic approaches, and some risks and safety aspects, before examining different types of functional mapping. Much of this review explores the prospects for new mapping approaches to better understand other components of language, memory, spatial skills, executive, and socio-emotional functions. We also touch on brain-machine interfaces, philosophical aspects of aligning tasks to brain circuits, and the study of consciousness. We end by discussing multi-modal testing and virtual reality approaches to mapping higher cortical functions.

Keywords: SEEG; cerebral cortex; connectivity; language; memory; passive mapping; socioemotional; stimulation mapping.

Figure 1

Historical sites central to the development of cognitive mapping: (A) the Montreal Neurological Institute, Montreal, Canada and (B) St. Anne Hospital, Paris, France.

Figure 2

Cortical activity during a receptive language task. From the first study of passive mapping of electrocorticographic activity during a receptive task of distinguishing tones from phonemes by Crone et al. (90). Indices during perception of tones (lower left inset with black border) vs. phonemes (expanded view of left temporal lobe). Plots of event-related power augmentation/suppression are color-coded according to frequency, and correspond to the electrode locations depicted in the upper left corner inset (white frame indicates borders of the expanded views). Detailed plots in the right column are derived from an electrode over the left superior temporal gyrus (circled). PSA, power spectral array; ESD, event related desynchronization; ERS, event related synchronization; EP, evoked potential. From Crone et al. (90).

Figure 3

Speech synthesis from recorded electrocorticogram during spoken sentences. (A) The neural decoding process begins by extracting relevant signal features from high-density cortical activity. (B) A neural network decodes kinematic representations of articulation from ECoG signals. (C) An additional algorithm decodes acoustics from the previously decoded kinematics. Acoustics are spectral features extracted from the speech waveform. (D) Decoded signals are then synthesized into an acoustic waveform. (E) Spectrogram shows the frequency content of two sentences spoken by a participant. (F) Spectrogram of synthesized speech from brain signals recorded simultaneously with the speech in (E). From Anumanchipalli et al. (106).

Figure 4

Example of using CCEPs to study effective connectivity. (A) Axial MRI Brian (T1) showing two periventricular nodular heterotopias in the tirgone of the left lateral ventricle and the trajectories of electrodes 7, 9, and 35, with dashes showing the approximate location of the 10 contacts of each recording electrode. (B) The approximate lateral entry points of pertinent left-sided SEEG electrodes are shown as electrode numbers. (C) An example spontaneous left sided seizure onset is shown with gamma activity on electrode 29 contacts 1–3 (posterior hippocampus). (D) Raw cortico-cortical evoked potentials (CCEPS) triggered by 1 Hz bipolar stimulation anterior heterotopion (electrode 7 contacts 3–4). Evoked potentials with peak to trough amplitude >250 μV are evident on electrodes 29, 31, and 33. (E) Averaged CCEPs with 20–50 ms (gray bar) window of interest shown. Only the largest amplitude (root mean squared amplitude (RMSA) CCEP (taken over all 10 contacts) is shown for each electrode. (F) Connectivity map for stimulation to electrode 7 (black) and 35 (gray). Thick arrows represent CCEPs with RMSA >200 mV, thin arrows represent CCEPs with RMSA >100 mV. From Dickey et al. (116).

Figure 5

Models and Anatomy of Language Networks reveal the large area of the cerebral cortical involved in language. Top left: Geschwind's (169) illustration of the Broca-Wernicke model of language. Top right: Indefrey's (170) model of cortical activity (showing evoked potential latencies by region in milliseconds) during a confrontation naming task, demonstrating some of the cortical extent of language processing. Bottom left: Hickok and Poeppel (39) dual stream model of the cortical anatomy of auditory language comprehension and response where auditory processing is bilateral and involves bilateral superior temporal sulci and unimodal auditory cortex from which activity is conveyed to either a dorsal stream for, motor and articulatory analysis and phonological representation, vs. a ventral stream for lexical and conceptual representation (aITS, anterior inferior temporal sulcus; aMTG, anterior middle temporal gyrus; pIFG, posterior inferior frontal gyrus; PM, premotor cortex). Bottom right: From Ulvin et al. (125), showing the regions in which stimulation can result in specific naming deficits (“positive naming sites”), identifying the crucial role of the dominant fusiform basal temporal region in naming (ITG, inferior temporal gyrus; FG, fusiform gyrus; OTS, occipitotemporal sulcus; PHG, parahippocampal gyrus).

Figure 6

Intraoperative use of the iPad-based Neuromapper tool. Written informed consent was obtained from the individual for the publication of any potentially identifiable images.

Figure 7

Emory Multimodal Memory Test. A new multimodal tool that is under development for the assessment of multiple domains of cognition and their integration, along with simultaneous recorded eye position and pupil diameter data. Written informed consent was obtained from the individual for the publication of any potentially identifiable images.

Figure 8

Examining meta-memory with a spatial task. In this virtual reality task from the Cleary Lab (258), subjects can rate feelings of familiarity and deja vu after flying through spatially similar scenes. This is presently being used in the setting of SEEG to examine the anatomy and network activity associated with familiarity. It is also under development with contemporary virtual reality hardware and software.