Face recognition is of major social importance and involves highly selective brain regions thought to be organized in a distributed functional network. However, the exact architecture of interconnections between these regions remains unknown. We used functional magnetic resonance imaging to identify face-responsive regions in 22 participants and then employed diffusion tensor imaging with probabilistic tractography to establish the white-matter pathways between these functionally defined regions. We identified strong white-matter connections between the occipital face area (OFA) and fusiform face area (FFA), with a significant right-hemisphere predominance. We found no evidence for direct anatomical connections between FFA and superior temporal sulcus (STS) or between OFA and STS, contrary to predictions based on current cognitive models. Instead, our findings point to segregated processing along a ventral extrastriate visual pathway to OFA-FFA and another more dorsal system connected to STS and frontoparietal areas. In addition, early occipital areas were found to have direct connections to the amygdala, which might underlie a rapid recruitment of limbic brain areas by visual inputs bypassing more elaborate extrastriate cortical processing. These results unveil the structural neural architecture of the human face recognition system and provide new insights on how distributed face-responsive areas may work together.

We determined the location, functional response profile, and structural fiber connections of auditory areas with voice- and emotion-sensitive activity using functional magnetic resonance imaging (fMRI) and diffusion tensor imaging. Bilateral regions responding to emotional voices were consistently found in the superior temporal gyrus, posterolateral to the primary auditory cortex. Event-related fMRI showed stronger responses in these areas to voices-expressing anger, sadness, joy, and relief, relative to voices with neutral prosody. Their neural responses were primarily driven by prosodic arousal, irrespective of valence. Probabilistic fiber tracking revealed direct structural connections of these “emotional voice areas” (EVA) with ipsilateral medial geniculate body, which is the major input source of early auditory cortex, as well as with the ipsilateral inferior frontal gyrus (IFG) and inferior parietal lobe (IPL). In addition, vocal emotions (compared with neutral prosody) increased the functional coupling of EVA with the ipsilateral IFG but not IPL. These results provide new insights into the neural architecture of the human voice processing system and support a crucial involvement of IFG in the recognition of vocal emotions, whereas IPL may subserve distinct auditory spatial functions, consistent with distinct anatomical substrates for the processing of “how” and “where” information within the auditory pathways.

Spontaneous fluctuations of neuronal activity in large-scale distributed networks are a hallmark of the resting brain. In relapsing-remitting multiple sclerosis (RRMS) several fMRI studies have suggested altered resting-state connectivity patterns. Topographical EEG analysis reveals much faster temporal fluctuations in the tens of milliseconds time range (termed "microstates"), which showed altered properties in a number of neuropsychiatric conditions. We investigated whether these microstates were altered in patients with RRMS, and if the microstates' temporal properties reflected a link to the patients' clinical features. We acquired 256-channel EEG in 53 patients (mean age 37.6 years, 45 females, mean disease duration 9.99 years, Expanded Disability Status Scale ≤ 4, mean 2.2) and 49 healthy controls (mean age 36.4 years, 33 females). We analyzed segments of a total of 5 min of EEG during resting wakefulness and determined for both groups the four predominant microstates using established clustering methods. We found significant differences in the temporal dynamics of two of the four microstates between healthy controls and patients with RRMS in terms of increased appearance and prolonged duration. Using stepwise multiple linear regression models with 8-fold cross-validation, we found evidence that these electrophysiological measures predicted a patient's total disease duration, annual relapse rate, disability score, as well as depression score, and cognitive fatigue measure. In RRMS patients, microstate analysis captured altered fluctuations of EEG topographies in the sub-second range. This measure of high temporal resolution provided potentially powerful markers of disease activity and neuropsychiatric co-morbidities in RRMS.