Statistical non-parametric mapping in sensor space

Abstract

Establishing the significance of observed effects is a preliminary requirement for any meaningful interpretation of clinical and experimental Electroencephalography or Magnetoencephalography (MEG) data. We propose a method to evaluate significance on the level of sensors whilst retaining full temporal or spectral resolution. Input data are multiple realizations of sensor data. In this context, multiple realizations may be the individual epochs obtained in an evoked-response experiment, or group study data, possibly averaged within subject and event type, or spontaneous events such as spikes of different types. In this contribution, we apply Statistical non-Parametric Mapping (SnPM) to MEG sensor data. SnPM is a non-parametric permutation or randomization test that is assumption-free regarding distributional properties of the underlying data. The method, referred to as Maps SnPM, is demonstrated using MEG data from an auditory mismatch negativity paradigm with one frequent and two rare stimuli and validated by comparison with Topographic Analysis of Variance (TANOVA). The result is a time-or frequencyresolved breakdown of sensors that show consistent activity within and/or differ significantly between event or spike types. TANOVA and Maps SnPM were applied to the individual epochs obtained in an evoked-response experiment. The TANOVA analysis established data plausibility and identified latencies-of-interest for further analysis. Maps SnPM, in addition to the above, identified sensors of significantly different activity between stimulus types.

Parallel fast and slow recurrent cortical processing mediates target and distractor selection in visual search

“…In contrast to the tERL, the dERL does not show modulation in the time range of the post-Nt. To verify time ranges of significant ERMF modulation underlying the tERL and dERL, one-way topographic analyses of variance (TANOVA) 24,25 with the factor-levels target or distractor in left versus right VF were computed for each time sample between 0 and 500 ms after stimulus onset (see "Methods" for details). The result is shown by the dashed traces below the magnetic waveforms, where significant time ranges are shown as grayed areas.…”

“…3The initial activity in higher-level ventral extrastriate areas is more dorsal for the Nt and more ventral and anterior for the Pd. To validate the topographical differences between the Nt and the Pd a statistical non-parametric mapping (SnPM 25 , see "Methods" for details) analysis with the factors VF (left/right) and component (Nt, Pd) was performed for both the normalized ERMFs and the CDRs (MNLS). Figure 6b shows the VF (left/right) × component (Nt/Pd) interaction, which yields cortical regions where the distribution of the Nt and Pd differ significantly.…”

Attention expedites target selection by prioritizing the neural processing of distractor features

“…The data were tested in the time range of 0 to 300ms after stimulus onset, the width of the sliding window was 11.8ms (i.e., three time samples). To correct for multiple comparisons, we followed the logic of Wagner et al (2017) taking into account the original sampling frequency (fs, here 254.31Hz) and the applied low-pass filter (fc, here 50Hz). The corrected alpha level was 1-(1-0.05) 2fc/fs ≈ 0.02.…”

Recent advances in biomagnetism and its applications

“…On the other hand, establishing the significance of observed effects is of importance for the interpretation of EEG or MEG data. In the article entitled ''Statistical Non-Parametric Mapping in Sensor Space'' [2], the authors applied statistical non-parametric mapping (SnPN), which is a nonparametric permutation test that does not need any assumption on the distribution properties of the data, to MEG sensor data. They showed that SnPN could effectively identify sensors of significantly different activity between stimulus types.…”

“…They showed that SnPN could effectively identify sensors of significantly different activity between stimulus types. It is expected that both articles [1,2] from the same research group would significantly contribute to improving the reliability and accuracy of MEG analyses.…”