Depression core network-based individualized targeting for transcranial magnetic stimulation.

BACKGROUND: Transcranial magnetic stimulation (TMS) of the dorsolateral prefrontal cortex (DLPFC) is an established treatment for major depressive disorder (MDD). Recent attempts to improve TMS efficacy by individually targeting DLPFC subregions that are functionally connected to the subgenual anterior cingulate cortex (sgACC) appear promising. However, sgACC covers only a small subset of core MDD-related areas. Further, fMRI connectivity of sgACC is poorly repeatable within subjects. METHODS: Based on an fMRI database analysis, we first constructed a novel core network model (CNM), capturing voxelwise emotion regulation- and MDD-related DLPFC connectivity. Then, in a sample of 15 healthy subjects and 29 MDD patients, we assessed (i) within-subject repeatability of the DLPFC connectivity patterns computed from time segments of varying lengths of individual-level fMRI data and (ii) association of MDD severity with the individual DLPFC connectivity strengths. We extracted group-level connectivity strengths in CNM from individual DLPFC coordinates stimulated with neuronavigated TMS in a separate sample of 25 MDD patients. These connectivity strengths were then correlated with individual TMS efficacy. RESULTS: Compared with sgACC connectivity, CNM increased intraindividual repeatability 5-fold. DLPFC connectivity strength from CNM was associated with MDD severity and TMS efficacy. While the locations of CNM-based individual TMS targets remained constant within individuals, they varied considerably between individuals. CONCLUSIONS: CNM increased repeatability of functional targeting to a clinically feasible level. The observed association of MDD severity and TMS efficacy with DLPFC connectivity supports the validity of the CNM. The interindividual differences in target locations motivate future individualized clinical trials leveraging the CNM.

A correlation-based method for extracting subject-specific components and artifacts from group-fMRI data.

We present a simple but effective correlation-based method (maxCorr) for extracting subject-specific components from group-fMRI data. The method finds signal components that correlate maximally with the data set of one subject and minimally with the data sets of the other subjects. We show that such subject-specific components are often related to movement and physiological noise (e.g. cardiac cycle, respiration). We further demonstrate that removing the most subject-specific components for each subject reduces the overall data variance and improves the statistical identification of true fMRI activations. We compare the performance of maxCorr with CompCor, a commonly used artifact-finding method in fMRI analysis. We show that maxCorr is less likely than CompCor to remove actual stimulus-related activity, especially when no information about the stimulus is available. MaxCorr operates without stimulus information and is therefore well suitable for analyses of fMRI experiments employing naturalistic stimuli, such as movies, where stimulus regressors are difficult to construct, and for brain decoding techniques benefiting from reduced subject-specific variance in each subject's data.

Activation of auditory cortex by anticipating and hearing emotional sounds: an MEG study.

To study how auditory cortical processing is affected by anticipating and hearing of long emotional sounds, we recorded auditory evoked magnetic fields with a whole-scalp MEG device from 15 healthy adults who were listening to emotional or neutral sounds. Pleasant, unpleasant, or neutral sounds, each lasting for 6 s, were played in a random order, preceded by 100-ms cue tones (0.5, 1, or 2 kHz) 2 s before the onset of the sound. The cue tones, indicating the valence of the upcoming emotional sounds, evoked typical transient N100m responses in the auditory cortex. During the rest of the anticipation period (until the beginning of the emotional sound), auditory cortices of both hemispheres generated slow shifts of the same polarity as N100m. During anticipation, the relative strengths of the auditory-cortex signals depended on the upcoming sound: towards the end of the anticipation period the activity became stronger when the subject was anticipating emotional rather than neutral sounds. During the actual emotional and neutral sounds, sustained fields were predominant in the left hemisphere for all sounds. The measured DC MEG signals during both anticipation and hearing of emotional sounds implied that following the cue that indicates the valence of the upcoming sound, the auditory-cortex activity is modulated by the upcoming sound category during the anticipation period.

Functional subdivision of group-ICA results of fMRI data collected during cinema viewing.

Independent component analysis (ICA) can unravel functional brain networks from functional magnetic resonance imaging (fMRI) data. The number of the estimated components affects both the spatial pattern of the identified networks and their time-course estimates. Here group-ICA was applied at four dimensionalities (10, 20, 40, and 58 components) to fMRI data collected from 15 subjects who viewed a 15-min silent film ("At land" by Maya Deren). We focused on the dorsal attention network, the default-mode network, and the sensorimotor network. The lowest dimensionalities demonstrated most prominent activity within the dorsal attention network, combined with the visual areas, and in the default-mode network; the sensorimotor network only appeared with ICA comprising at least 20 components. The results suggest that even very low-dimensional ICA can unravel the most prominent functionally-connected brain networks. However, increasing the number of components gives a more detailed picture and functionally feasible subdivision of the major networks. These results improve our understanding of the hierarchical subdivision of brain networks during viewing of a movie that provides continuous stimulation embedded in an attention-directing narrative.