Niko Mäkelä

A novel approach for documenting naming errors induced by navigated transcranial magnetic stimulation

Transcranial magnetic stimulation (TMS) is widely used both in basic research and in clinical practice. TMS has been utilized in studies of functional organization of speech in healthy volunteers. Navigated TMS (nTMS) allows preoperative mapping of the motor cortex for surgical planning. Recording behavioral responses to nTMS in the speech-related cortical network in a manner that allows off-line review of performance might increase utility of nTMS both for scientific and clinical purposes, e.g., for a careful preoperative planning. Four subjects participated in the study. The subjects named pictures of objects presented every 2-3s on a computer screen. One-second trains of 5 pulses were applied by nTMS 300ms after the presentation of pictures. The nTMS and stimulus presentation screens were cloned. A commercial digital camera was utilized to record the subjects performance and the screen clones. Delays between presentation, audio and video signals were eliminated by carefully tested combination of displays and camera. An experienced neuropsychologist studied the videos and classified the errors evoked by nTMS during the object naming. Complete anomias, semantic, phonological and performance errors were observed during nTMS of left fronto-parieto-temporal cortical regions. Several errors were detected only in the video classification. nTMS combined with synchronized video recording provides an accurate monitoring tool of behavioral TMS experiments. This experimental setup can be particularly useful for high-quality cognitive paradigms and for clinical purposes.

Effects of navigated TMS on object and action naming

Transcranial magnetic stimulation (TMS) has been used to induce speech disturbances and to affect speech performance during different naming tasks. Lately, repetitive navigated TMS (nTMS) has been used for non-invasive mapping of cortical speech-related areas. Different naming tasks may give different information that can be useful for presurgical evaluation. We studied the sensitivity of object and action naming tasks to nTMS and compared the distributions of cortical sites where nTMS produced naming errors. Eight healthy subjects named pictures of objects and actions during repetitive nTMS delivered to semi-random left-hemispheric sites. Subject-validated image stacks were obtained in the baseline naming of all pictures before nTMS. Thereafter, nTMS pulse trains were delivered while the subjects were naming the images of objects or actions. The sessions were video-recorded for offline analysis. Naming during nTMS was compared with the baseline performance. The nTMS-induced naming errors were categorized by error type and location. nTMS produced no-response errors, phonological paraphasias, and semantic paraphasias. In seven out of eight subjects, nTMS produced more errors during object than action naming. Both intrasubject and intersubject analysis showed that object naming was significantly more sensitive to nTMS. When the number of errors was compared according to a given area, nTMS to postcentral gyrus induced more errors during object than action naming. Object naming is apparently more easily disrupted by TMS than action naming. Different stimulus types can be useful for locating different aspects of speech functions. This provides new possibilities in both basic and clinical research of cortical speech representations.

Dealing with artifacts in TMS-evoked EEG

The artifact problem in TMS-evoked EEG is analyzed in an attempt to clarify the nature of the problem and to present solutions. The best way to deal with artifacts is to avoid them; the removal or suppression of the unavoidable artifacts should be based on accurate information about their characteristics and the properties of the signal of interest.

Truncated RAP-MUSIC (TRAP-MUSIC) for MEG and EEG source localization

&NA; Electrically active brain regions can be located applying MUltiple SIgnal Classification (MUSIC) on magneto‐ or electroencephalographic (MEG; EEG) data. We introduce a new MUSIC method, called truncated recursively‐applied‐and‐projected MUSIC (TRAP‐MUSIC). It corrects a hidden deficiency of the conventional RAP‐MUSIC algorithm, which prevents estimation of the true number of brain‐signal sources accurately. The correction is done by applying a sequential dimension reduction to the signal‐subspace projection. We show that TRAP‐MUSIC significantly improves the performance of MUSIC‐type localization; in particular, it successfully and robustly locates active brain regions and estimates their number. We compare TRAP‐MUSIC and RAP‐MUSIC in simulations with varying key parameters, e.g., signal‐to‐noise ratio, correlation between source time‐courses, and initial estimate for the dimension of the signal space. In addition, we validate TRAP‐MUSIC with measured MEG data. We suggest that with the proposed TRAP‐MUSIC method, MUSIC‐type localization could become more reliable and suitable for various online and offline MEG and EEG applications. Graphical abstract Figure. No caption available. HighlightsA modified RAP‐MUSIC method to locate multiple brain sources in EEG/MEG is proposed.The new method, TRAP‐MUSIC, solves a hidden deficiency in the original algorithm.TRAP‐MUSIC applies a recursive dimension reduction of the signal‐space estimate.The improvements in performance come without any computational cost.

Locating highly correlated sources from MEG with (recursive) (R)DS-MUSIC

We introduce a source localization method of the MUltiple Signal Classification (MUSIC) family that can locate brain-signal sources robustly and reliably, irrespective of their temporal correlations. The method, double-scanning (DS) MUSIC, is based on projecting out the topographies of source candidates during topographical scanning in a way that breaks the mutual dependence of highly correlated sources, but keeps the uncorrelated sources intact. We also provide a recursive version of DS-MUSIC (RDS-MUSIC), which overcomes the peak detection problem present in the non-recursive methods. We compare DS-MUSIC and RDS-MUSIC with other localization techniques in numerous simulations with varying source configurations, correlations, and signal-to-noise ratios. DS- and RDS-MUSIC were the most robust localization methods; they had a high success rate and localization accuracy for both uncorrelated and highly correlated sources. In addition, we validated RDS-MUSIC by showing that it successfully locates bilateral synchronous activity from measured auditory-evoked MEG.

O152 Investigating effective connectivity in the motor network with TMS-evoked cortical potentials

Objectives Our purpose was to learn what the spatial distribution of transcranial magnetic stimulation (TMS)-evoked potentials can reveal about connectivity originating from premotor, supplementary, and primary motor cortices. Methods The data were collected with the combination of navigated TMS (nTMS) and EEG from four subjects (one subject reported here). The primary, pre-, and supplementary motor cortices in both hemispheres were stimulated, each area receiving 150 pulses at stimulation intensity of 90% of the electric field of ipsilateral APB motor threshold. The EEG datasets were preprocessed with novel artifact-removal algorithms (Mutanen et al., NeuroImage 2016). The first four peaks and their latencies were determined from the global mean field amplitudes (GMFA). At these peak latencies, minimum-norm estimates (MNE) indicated sites of most prevalent cortical activity. Results The new artifact-removal method proved to be useful as the first step in the data analysis. The spreading of neuronal activity depends on the stimulation target; the order of the activated cortical areas varies when different motor-related areas are stimulated. Discussion Our combination of experimental settings, data processing tools, and data-analysis methods can be used to evaluate effective connections from motor areas. Cortical activation patterns differ depending on the stimulated motor area. Conclusions nTMS–EEG can be used to investigate the connectivity originating from motor areas. Significance nTMS–EEG may be used to select stimulation sites on the cortex when specific neuronal connections should be strengthened by TMS, for example, in stroke patients.